CROSS REFERENCE TO RELATED APPLICATION

This invention is related to commonly-assigned U.S. patent application Ser. No. 09/372,377 filed on Aug. 11, 1999 for “Systems For Identifying Connotative Meaning,” now pending; commonly-assigned U.S. patent application Ser. No. 09/372,549 filed on Aug. 11, 1999 for “System for Quantifying Intensity of Connotative Meaning,” now pending; commonly-assigned U.S. patent application Ser. No. 09/372,243 filed on Aug. 11, 1999 for “Interactive Connotative Dictionary,” now pending; and commonly-assigned U.S. patent application Ser. No. 09/372,244 filed on Aug. 11, 1999 for “Interactive Connotative Thesaurus System,” now pending. The content of all such applications are incorporated herein by reference and made a part hereof.

BACKGROUND OF THE INVENTION

This invention relates to a computerized interactive system of language analysis, and more particularly to a system for the connotative analysis of discourse.

Language is an abstract, rule-governed system of arbitrary symbols that can be combined in countless ways to communicate information. All languages include a system of phonology (i.e., set of sounds), semantics (i.e., word, phrase and sentence meanings), morphology (i.e., rules for combining smallest meaningful units to form or alter words), syntax (i.e., ways in which words are organized into phrases and sentences) and pragmatics (i.e., rules governing a conversation and social use of language).

The use of language enables humankind to develop advanced cognitive abilities. Cognitive development relates to the changes in a person's memory, thinking, use of language and other mental skills as they develop from infants to adults. Humans develop a certain degree of cognitive competence. In addition to such cognitive competence, humans also display and experience feelings, emotions and moods. In particular, our emotional state or the emotional state we desire to elicit can influence our choice of words. Every human language enables people to communicate both intellectually and emotionally because words and phrases convey both cognitive and affective meaning. ‘Affective’ means to be influenced by or result from emotions.

Linguistics is the scientific study of language. Semantics is the branch of linguistics that deals with the study of the relationship between words or phrases and their meanings. Of particular significance here are the contrasting linguistic terms, denotation and connotation. ‘Denotation’ is a particular meaning of a symbol. ‘Connotation’ is an idea or meaning suggested by or associated with a word or phrase. Thus, ‘denote’ describes the relation between a word or phrase and the thing it conventionally names, whereas ‘connote’ describes the relation between the word or phrase and the images or associations it evokes. As used herein a denotation is an objective, cognitive meaning which refers to the direct relationship between a term and the object, idea or action it designates. As used herein, a connotation is a subjective, affective meaning which refers to the emotive and associative aspect of a term.

The denotative meanings of words have been systematically codified into definitions and collected together to form dictionaries, thesauruses and related denotative language references. However, the codification of connotative meanings has not been achieved. Consider, for example, a dictionary which provides the following denotative meaning for the word ‘pub’: “a building providing alcoholic drinks for consumption on the premises” (Oxford Dictionary). However, the word ‘pub’ simultaneously conveys a host of emotional connotations, such as merriment, pleasure, cheerfulness, perhaps some sadness, and so on. Similarly, words such as ‘summer’, ‘love’, and ‘melody’ have a variety of positive emotional connotative associations for most people, while words such as ‘cancer’, ‘rape’, and ‘homeless’ have negative emotional connotations for most people. In all cases, the associated connotations are not systematically accessible using any known language reference resource or tool.

The reason for the absence of codification of connotative meaning is that, while words readily evoke emotional connotations, the converse is not true: emotional connotations are not easily codified using words. Unlike denotative meaning, affective meaning does not naturally lend itself to systematic word-symbol description. Emotions are felt, not thought, so the relationship between a word and its associated connotative content, while real, is not codifiable using the relatively straightforward methods employed by lexicographers in fashioning denotative definitions. Accordingly, there is a need for a connotation language reference tool and a system for codifying the connotative content of such a tool.

Not only is it desirable to identify connotative meaning, it also is desirable to quantify the intensity associated with a connotation. Some words or phrases evoke stronger responses than others. Some words or phrases are more activity oriented. In the 1950s, Charles Osgood, an American psychologist developed a method of constructing bipolar scales based on semantic opposites, such as “good-bad”, “soft-hard”, “fast-slow,” “clean-dirty,” “valuable-worthless,” “fair-unfair,” and so on. Osgood called these scales “semantic differential” scales because they differentiated attitudinal intensity based on a person's subjective understanding of the connotative meanings of words. Osgood et al. explored large amounts of data provided by students who evaluated numerous words and phrases on numerous semantic differential scales. The outcome of Osgood's investigations was a description of the existence of “Semantic space,” three measurable underlying attitudinal dimensions that proved in subsequent research to be robustly identifiable across other languages and cultures. Osgood named these dimensions Evaluation, Power, and Activity (EPA). Experimentation by many investigators around the world confirmed the reality of semantic space and its cross-cultural validity (Japan, Scandinavia, Germany, Ireland etc.).

The semantic differential is a method for measuring the meaning of an object to an individual. It may also be thought of as a series of attitude scales. A subject is asked to rate a given concept (for example, ‘Irish’, ‘Republican’, ‘wife’, ‘me as I am’) on a series of seven-point bipolar rating scales. Any concept—whether it is a political issue, a person, an institution, a work of art—can be rated. Subgroups of the scales can be summed up to yield scores that are interpreted as indicating the individual's position on three underlying dimensions of attitude toward the object being rated. These dimensions have been identified by using factor-analytic procedures in examining the responses of many individuals concerning many concepts or objects. It has been found that three subgroups measure the following three dimensions of attitude: (1) the individual's evaluation of the object or concept being rated, corresponding to the favorable-unfavorable dimension in more traditional attitude scales; (2) the individual's perception of the potency or power of the object or concept; and (3) the individual's perception of the activity of the object or concept. (See Kidder, L. H., “Research Methods in Social Relations;” 1981).

The problem with the semantic differential technique is that it does not distinguish beyond a single evaluative continuum, with positive attitude at one end of the scale through negative attitude at the other end. That is, it does not actually identify any individual emotions. Moreover, although several short “semantic differential dictionaries” have been developed (known in the literature as semantic “atlases” because they are analogous to “maps” of semantic space), consisting of 500 to 1,500 words with EPA scores for each word, the technique of semantic differential is not associated with any system or method for codifying the words of any given language, even on a single affective variable. According there is a need for a system for codifying the connotative meaning of words.

Computerized systems for analyzing language include various language parsing systems and grammar checkers. Such systems relate more to the mechanical analysis of discourse rather than to the content and quality of discourse.

In general written discourse varies widely in emotional and imaginative content, where works more imaginative tend to rely more on connotative content to impact the intended audience of the discourse. Pure non-fiction, which is primarily informational in content is at one end of a continuum ranging from non-imaginative to imaginative discourse. Such pure non-fiction works are mainly comprised of words having comparatively little emotionally-associated connotative content. These documents tend to be objective, impersonal, and occupation-specific, such as academic and scientific research papers; business textbooks; administrative documents; and technical manuals.

At the other end of the continuum is literary art. Such works contain many words and idioms that are charged with emotional and human-interest connotations. These works tend to be subjective, personal, leisure-oriented, and appealing to the imagination. They include novels, short stories, plays, movie and TV scripts, poetry, and song lyrics.

In the middle are many varieties of non-fiction that employ imaginative elements. Their authors try to present information while using vocabulary with sufficient connotative content to hold the interest and attention of readers. This type of writing includes: magazine and newspaper journalism; public relations and advertising messages; travel writing; self-help; scripted speeches; and religious writing such as sermons.

Although there are many fields of writing where connotative content is important to the quality of the work, there have not been tools for evaluating the connotative content of discourse. In particular, there has not been any system which analyzes the emotional meaning and impact of written discourse. Accordingly, there is a need for a system for connotative analysis of discourse.

SUMMARY OF THE INVENTION

According to the invention, a computerized interactive language reference system is provided for analyzing connotative content of discourse. The system is based upon a database of terms (i.e., words and phrases) and their associated denotative and connotative meanings.

According to one aspect of the invention, a data base is generated which stores the denotative context and connotative meanings of a plurality of words and phrases. Connotative meaning, along with the intensity of such meaning, are identified using a statistical model of sampled responses from select panels of evaluators.

According to another aspect of the invention, areas of human interest also are associated with a given term and its denotative meaning. Further, scaled ratings of the power, activity and abstract/concrete qualities of the term are maintained.

According to another aspect of the invention, connotative meanings for any given term are identified from a range of emotional descriptor terms. There are a plurality of predefined categories of emotional descriptors. In one embodiment described below for the English language there are 8 categories. In the preferred embodiment there are four categories of positive emotions (e.g., affection/friendliness, enjoyment/elation, amusement/excitement and contentment/gratitude) and four categories of negative emotions (e.g., sadness/grief, anger/loathing, fear/uneasiness, and humiliation/shame). Within each category there are a predefined list of emotional descriptors. A term may have a connotative meaning in any or all of the emotional categories. Some terms may not have any connotative meaning. In some embodiments only one emotional descriptor is permitted to be assigned for a given emotional category for a given term. Thus, for an eight category embodiment, any term can have 0 to 8 emotional descriptors—the emotional descriptors being from different emotional categories. In other embodiments a primary and a secondary emotional descriptor may be assigned for any given term. For such an embodiment, which is based on 8 emotional categories, any term can have 0-16 emotional descriptors—the emotional descriptors being in pairs, where the two emotional descriptors in a given pair being for a given emotional category. Different pairs include emotional descriptors for different emotional categories.

According to another aspect of the invention, for each emotional descriptor associated with a given term, there is an intensity rating of how strongly or intensely the term tends to promote or relate to the emotional descriptor.

According to another aspect of the invention, all or a portion of a work of written discourse is analyzed for connotative content. Such portion of work is stored in computer-readable format and input to the system. In one example, a user selects a passage to analyze. The system goes through each word of the passage and determines whether there is an entry in the database for such word. The system also identifies phrases with entries in the data base. For each term (i.e., word or phrase) where there is an entry in the database, there is a check to see if the term has more than one denotative meaning.

According to another aspect of the invention where there is more than one denotative meaning, one of the denotative meanings is selected. Such selection is either done manually by prompting the user to select which denotative meaning is intended, or is done automatically by analyzing the context of the words to select the one denotative meaning which is most likely related to the contextual use of the term in the passage.

According to another aspect of the invention, data in the database for the terms in the selected passage are analyzed. The passage is evaluated at different levels of detail for connotative content. At one level of analysis, the passage is evaluated for positive emotional connotations, negative emotional connotations, global emotional connotations, human interest data, connotations of power, connotations of activity and connotations of abstractness or concreteness.

According to another aspect of the invention, the connotations for each term are tallied to accumulate ratings of emotional and non-emotional connotations.

According to another aspect of the invention, a measure of human interest is determined by calculating the proportion of pronouns, references to gender, characters' names, and other direct references to people relative to the total number of terms or words in the passage selected.

According to another aspect of the invention, a more detailed level of analysis also is performed in which the dominant emotional connotations in the passage are identified and ranked.

According to another aspect of the invention, terms which are dominant in terms of connotative impact are identified. Dominant terms are identified as a function of the number of occurrences of the term/denotative context and the various intensity ratings for each emotional descriptor associated with such term in the context used.

According to one advantage of the invention a large amount of connotative and denotative information is accessible to an end user in a manageable format. According to another advantage of the invention, the data is updated over time for changing connotations. In particular, for a distributed computing model of implementation, such as a global computer network, the content may be updated continually or at varying intervals.

According to another advantage of the invention, an author, editor or another is able to automatically analyze the discourse to view the connotative aspects of the work. Such author editor or another interested party then can go back and alter the choice of some words, phrases, or even passages to fit more closely to a connotative profile desired for the discourse. In particular, by iteratively editing and analyzing the discourse a more desirable and effective discourse can be obtained for a given target audience. These and other aspects and advantages of the invention will be better understood by reference to the following detailed description taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1

is a block diagram of a host computer system;

FIG. 2

is a diagram of a system for connotative analysis of discourse according to an embodiment of this invention;

FIG. 3

is a display sample of a user interface according to an embodiment of this invention;

FIG. 4

is a flow chart of a method for analyzing discourse according to an embodiment of this invention;

FIG. 5

is a display sample of the user interface for a level

1

analysis of discourse;

FIG. 6

is a display sample of the user interface for a level

2

analysis of discourse;

FIG. 7

is a display sample of the user interface for a level

3

analysis of discourse;

FIG. 8

is another display sample of the user interface for the level

3

analysis of discourse; and

FIG. 9

is a diagram of a system for identifying connotative meanings according to an embodiment of this invention.

DESCRIPTION OF SPECIFIC EMBODIMENTS

A system and method are described for identifying, codifying, and storing connotative meaning inherent in the words and phrases of any language, along with a system for connotatively analyzing discourse. Throughout this description, a preferred embodiment and examples given should be considered as exemplars rather than limitations on the method and system of the present invention.

Many of the functions of the present inventions preferably are performed by or with the assistance of a programmed digital computer of the type which is well known in the art, an example of which is shown in

FIG. 1. A

computer system

20

has a display

22

, a key entry device

24

, a pointing/clicking device

26

, a processor

28

, and random access memory (RAM)

30

. In addition there commonly is a communication or network interface

34

(e.g., modem; ethernet adapter), a non-volatile storage device such as a hard disk drive

32

and a transportable storage media drive

36

which reads transportable storage media

38

. Other miscellaneous storage devices

40

, such as a floppy disk drive, CD-ROM drive, zip drive, bernoulli drive or other magnetic, optical or other storage media, may be included. The various components interface and exchange data and commands through one or more busses

42

. The computer system

20

receives information by entry through the key entry device

24

, pointing/clicking device

26

, the network interface

34

or another input device or input port. The computer system

20

may be any of the types well known in the art, such as a mainframe computer, minicomputer, or microcomputer and may serve as a network server computer

12

, remote network

16

computer or a client computer

14

. The computer system

20

may even be configured as a workstation, personal computer, network server, or a reduced-feature network terminal device.

System for Analyzing Connotative Discourse

Referring to

FIG. 2

, a connotative language analysis system

10

is formed by a data base

12

, a user interface

14

and various analytical processes

16

. The system

10

receives an input discourse

17

to be processed. In some embodiments the input discourse

17

is a file or other digitally encoded version of textual discourse. Preferably the data base

12

, interface

14

and processes

16

are implemented in electronic format as one or more software programs executed on a host computer system or over a host computer network. For example, the system

10

may be stored on an optical disc (e.g., CD-ROM) or other storage media and installed onto the host computer system or network. Specifically, the data base

12

, user interface software

14

and analytical processes

16

may be loaded and installed locally onto the host computer system or network. In some implementations the software embodying the user interface

14

and processes

16

are installed to be resident on the host computer system or network, while the data base

12

is stored and accessed from a removable storage media, such as an optical disk. In other embodiments the data base

12

is centrally located among one or more network server computers, while the user interface software

14

and processes

16

are stored and executed from either a local end user computer system or remotely at the server computer. The implementations may vary from local copies for a given end user's access to one or more copies stored on a private network or even a global computer network which users log into to access and use the dictionary.

Data Base

12

In a preferred embodiment of the data base

12

, the data base

12

includes a set of denotative fields

44

, a set of connotative fields

46

, and a set of human interest and miscellaneous fields

48

. The denotative fields and human interest/miscellaneous fields are not necessary, but provide additional information to the user. In some embodiments the data base

12

includes only the set of connotative fields

46

.

The set of denotative fields

44

includes at least two fields, and preferably three fields. In one implementation the denotative data is obtained from one or more electronic or print-based dictionaries in any language. Database records may be created for each word or phrase of the language, which may be the English language or any other language. In some embodiments the connotative language reference system may be specialized for a given subject (e.g., medical/health contexts; science). In other embodiments a general language reference is implemented. Table 1 below lists the two desired fields and one optional field included among the denotative fields:

TABLE 1

Denotative Field Types

1. Term (i.e., Word or phrase/idiom)

2. Specific denotative context

3. Part of speech—(optional)

For each record in the data base

12

, one denotative field is assigned to the word or phrase. A second denotative field is assigned to the denotative context (dictionary meaning) of the word or phrase. A third denotative field is assigned to the part of speech. Preferably, each context of each word is assigned a separate database record. Thus, if the dictionary definition of a single word has two meanings among a total of five denotative contexts, then there are five records, one for each context. There may be multiple contexts for a given dictionary meaning when, for example, there are different parts of speech for the word/meaning.

Assigning an additional field to track the meanings that subsume the contexts is not necessary to practice the invention, but such a field may be assigned in an alternative embodiment. The total number of records required is equal to the sum of all of the denotative contexts of all of the words in the dictionary or other denotative data sources.

In a preferred embodiment the set of connotative fields

46

includes a block of one or more fields for each one of a plurality of emotional categories. In a best mode embodiment eight emotional categories have been identified, and thus, eight blocks of connotative fields are included. In other embodiments the number of categories may vary to be more or less than eight. The number of fields comprising each block may vary. In one embodiment the number of fields in a given category corresponds to the maximum number of emotional descriptors for such category that may be associated with a given term and denotative context (i.e., with a database record). Although the method for defining connotative meaning may vary, in one embodiment the data is collected from multiple sources. In a given implementation the sources are judges or panels of judges. In an embodiment where one emotional descriptor is stored per emotional category, the primary emotional descriptor selected by the panel of judges is stored in the data base. In another embodiment there are two fields per emotional category used to store a primary emotional descriptor and a secondary emotional descriptor.

In another embodiment, there is a separate field in each block for each of a prescribed number of sample judges. In an example, where 24 connotative judges are used, each of the eight blocks of fields

46

includes 24 individual fields, one to hold each judge's emotional descriptor data for each word in each context. Several additional fields are reserved to hold calculated data based on connotative judges' emotional descriptor data. Fewer or more judges may be used, adjusting the number of fields within each block accordingly. Where each judges data is stored, an additional field or fields are included per emotional category to identify the primary emotional descriptor, or both the primary and secondary emotional descriptor, selected by the plurality of judges.

Table 2 lists eight emotional categories corresponding to the eight blocks of fields

46

for a best mode embodiment. The general emotional classification “Positive Emotions” subsumes four emotional categories, and the general emotional classification “Negative Emotions” subsumes the other four emotional categories, as practiced in a preferred embodiment of the invention. Each emotional category subsumes a list of 12 to 37 specific emotional descriptors, each of which is associated with a two-digit identifying code number. The specific code number may vary. Further, the manner in identifying the distinct descriptors also may vary. The total number of emotional descriptors in this example is 164. In various embodiments one or more emotional descriptors may be removed from this list, entire emotional categories may be removed from the list, or one or more emotional categories and descriptors may be added to this list.

The connotative descriptors that appear in Table 2 below are English language connotative descriptors for one embodiment of a general connotative language reference system. The specific words that make up the emotional connotative descriptors may vary. Of course such descriptors will vary from language to language.

TABLE 2

Connotative Database Fields

POSITIVE EMOTIONS:

Affection/

Amusement /

Enjoyment/

Contentment/

Friendliness

Excitement

Elation

Gratitude

01 Adoration

01 Amazement

01 Admiration

01 Appreci-

02 Affection

02 Amusement

02 Bliss

ation

03 Amorousness

03 Astonishment

03 Cheer

02 Comfort

04 Devotion

04 Eagerness

04 Delight

03 Content-

05 Fondness

05 Enthusiasm

05 Ecstasy

ment

06 Friendliness

06 Excitement

06 Elation

04 Gladness

07 Infatuation

07 Exhilaration

07 Enjoyment

05 Gratitude

08 Kindliness

08 Exuberance

08 Euphoria

06 Hope

09 Liking

09 Fun

09 Exultation

07 Peaceful

10 Love

10 Glee

10 Happiness

ness

11 Lust

11 Hilarity

11 Joy

08 Relief

12 Passion

12 Merriment

12 Jubilation

09 Satisfaction

13 Tenderness

13 Mirth

13 Pleasure

10 Serenity

14 Trust

14 Surprise

14 Pride

11 Thankful-

15 Warmth

15 Thrill

15 Rapture

ness

16 Wonder

12 Well-being

NEGATIVE EMOTIONS

Fear/

Anger

Humiliation

Sadness/Grief

Uneasiness

/Loathing

/Shame

01 Affliction

01 Alarm

01 Abhorrence

01 Chagrin

02 Agony

02 Anxiety

02 Acrimony

02 Contrition

03 Anguish

03 Apprehension

03 Aggravation

03 Degradation

04 Dejection

04 Desperation

04 Anger

04 Discredit

05 Demoralization

05 Distress

05 Animosity

05 Disgrace

06 Depression

06 Dread

06 Annoyance

06 Dishonor

07 Desolation

07 Fear

07 Antagonism

07 Disrepute

08 Despair

08 Horror

08 Antipathy

08 Disrespect

09 Despondency

09 Nervousness

09 Aversion

09 Embarrass-

10 Disappointment

10 Panic

10 Bitterness

ment

11 Discourage-

11 Paranoia

11 Contempt

10 Guilt

ment

12 Stress

12 Creepiness

11 Humiliation

12 Disheartenment

13 Tension

13 Detestation

12 Indignity

13 Disillusionment

14 Terror

14 Dissatis-

13 Mortifi-

14 Dismay

15 Uneasiness

faction

cation

15 Distress

16 Worry

15 Disdain

14 Regret

16 Downhearted-

16 Disgust

15 Remorse

ness

17 Dislike

16 Shame

17 Forlornness

18 Enmity

17 Stigma

18 Gloom

19 Envy

19 Grief

20 Exasper-

20 Heartache

ation

21 Heartbreak

21 Frustration

22 Heartsickness

22 Fury

23 Hopelessness

23 Hatred

24 Hurt

24 Hostility

25 Longing

25 Irritation

26 Melancholy

26 Indignation

27 Misery

27 Ire

28 Pain

28 Jealousy

29 Pity

29 Loathing

30 Sadness

30 Offense

31 Sorrow

31 Outrage

32 Suffering

32 Rage

33 Torment

33 Rancor

34 Unhappiness

34 Resentment

35 Wretchedness

35 Vexation

36 Yearning

36 Virulence

37 Wrath

In an embodiment including a set of human interest fields

48

, the fields

48

relate a record and its denotative context to a human interest category. The purpose of incorporating the human interest fields is to permit the end user to easily retrieve special connotative content from the database by first selecting one or more human interest filters before querying the database. The human interest fields employed in a preferred embodiment of the invention are listed in Table 3. There are nine groupings of the human interest categories in such embodiment. Each human interest category is a field having a descriptor word. The categories and descriptors may vary from embodiment to embodiment.

TABLE 3

SET 3: Human Interest Database Fields

Non-emotional Connotations

Spiritual Identity

Power

Biblical Diction

Activity

Christianity

Judaism

Rhythm

Islam

Number of Syllables

Hinduism

Accented Syllable

Buddhism

Other Religious

Special Diction

Myth and Legend

Question-starting Words

Paranormal

Core Words Identified by S. I.

Hayakawa

Physical Identity

Physical Appearance

Personal Identity

Body

Gender

Health

First Names (Baby Names)

Notorious Or Celebrated People

Perception

Languages

Abstract/Concrete Continuum

National Identity

Place, General

Organizations of Note

Place, Event

Home

Place, Transportation

Personal Relationships

Place, Cosmos

Intimacy

Place, Noted

SET 3: Human Interest Fields

Perception (cont'd)

Non-human Life

Color

Animals

Hearing

Plants

Touch

Micro Organisms

Taste

Smell

Argot/Vernacular

Time, General

Slang

Time, Historical

Taboo

Time, Calendar

Offensive

Non-medical Drug Use

Derogatory

Disgusting/Revolting

Euphemistic

Cliche

In a preferred embodiment there are also a set of miscellaneous fields

48

for storing respective ratings of power, activity and abstractness versus concreteness. In a preferred embodiment of the invention, the assignment of the fields and records as described above effectively links each traditional dictionary definition of each word in each context with more than 200 connotative, human interest and miscellaneous variables. The data base is organized into one or more tables, files or other units of organizing data. Regardless of the number of tables or files, there are entries from each table or file which correspond to a term (i.e., a word or phrase among the denotative fields). The data associated with any given term is referred to herein and in the claims as a ‘record’, whether or not coming from a single table or file, or from multiple tables or files. Each record includes a field which identifies a term and another field which identifies the denotative context for the term. The record also includes a field which identifies a connotative meaning. Thus, each record includes at least three fields allocated among 1 to 3 tables, files or other units of organization of the data base. In a preferred embodiment there are a plurality of connotative meaning fields, at least one for each of a plurality of emotional categories. The connotative meaning fields identify an emotional descriptor for a corresponding emotional category. Such identification may be an identification of a specific emotional descriptor within the corresponding category or an indication that there is no emotional descriptor for such category. An indication of no connotative meaning is still considered to be connotative meaning information.

In varying embodiments additional fields are included for any one or more of the following types of information: parts of speech, intensity of connotative meaning, power rating, activity rating, abstract/concrete rating, human interest areas. When an intensity field is included the intensity is an intensity of a connotative meaning and is associated with a corresponding emotional category and the identified emotional descriptor for such category.

User Interface

14

and Analyzing Processes

16

In a preferred embodiment a graphical user interface

14

is implemented, which provides an end user with the capability of analyzing a passage of discourse for connotative content. Although there are many ways in which a user interface may be implemented, in one embodiment a system with menus and windows is used.

Referring to

FIG. 3

, in one embodiment the user interface

14

includes a window

52

having an area

55

for displaying denotative information from the database

12

and an area

57

with varying content. Referring to

FIG. 4

, a process

50

for analyzing a passage for connotative content is shown. At step

51

, input discourse

17

(see

FIG. 2

) is loaded and a passage

54

(see

FIG. 3

) of discourse is selected. At step

56

, an operator presses a button

53

to commencing scanning the passage

54

. During the scanning, the passage

54

is evaluated to identify words and phrases in the passage that correspond to terms in the data base

12

. As previously described, there is a separate record in the data base for each denotative context of each term. Thus, for some terms there may be multiple records in the data base

12

. In one embodiment, one of the multiple records is automatically selected and associated with the term of the passage. Such selection is based on probabilities or a language distinctive context interpretation system. For example U.S. Pat. No. 5,794,050 to Dahlgren et al. discloses a system for interpreting natural language input. In other embodiments or at the user's option, the user is prompted to select the appropriate denotative context (and thus the appropriate record). Such user selection has the advantage of being more accurate, but the disadvantage of being slower. Referring to

FIG. 3

the user is being prompted to select an appropriate denotative context for the term “wedding.” As shown in area

55

there are three denotative contexts for “wedding.” From viewing the passage

54

, the user can determine that the first context is correct. In one embodiment the user simply clicks on the first definition to make their selection. The scanning process then continues. In some embodiments two passes are performed. During a first pass, all words and phrases in the passage that have only one denotative context are identified separate from those that have multiple denotative contexts. During a second pass, the system prompts the user to select the appropriate denotative context for each term having multiple denotative contexts in the database

12

.

Referring again to

FIG. 4

, at step

58

a set of analytical processes are executed to derive connotative information pertaining to the passage. The derived connotative information then is displayed at step

59

in area

57

of the display window

52

(see FIGS.

5

-

8

). Referring to

FIG. 5

, there are three tabs

62

,

64

,

66

corresponding to three levels of information.

FIG. 5

shows the information at a first level where tab

62

is selected. Display box

61

shows the title of file name of the input discourse

17

or passage

54

.

The high level information of

FIG. 5

includes a rating of the passage for emotional content in terms of positive emotion, negative emotion and global emotion (i.e., positive not distinct from negative emotion). The high level information also includes a rating of the passage for human interest by words, sentences and global content. a rating of the passage for power, a rating of the passage for activity and a rating of the passage along a scale of abstractness versus concreteness.

Emotional content of the passage is derived by counting the number of emotional connotations associated with all the terms in the passage. Each term in the passage corresponds to 0 or one record in the database. Each record in the database

12

has at least one connotative field for each one of a plurality of emotional categories. For an embodiment where only one emotional descriptor is associated with a record per emotional category, there can be 0 to n emotional connotations per record (or per term in the passage), where n corresponds to the number of emotional categories. In some embodiments there are two connotative fields per emotional category. In such embodiment there can be 0 to 2n emotional connotations per record or per term in the passage.

A rating of global emotion is a cumulative count of the total number of emotional connotations for all terms of the passage. Referring to

FIG. 5

a bar graph

68

is displayed indicating the global emotion rating of the passage. Such rating is based on a scale. The scale is shown to vary from slight to infinite, although the scale referencing may vary.

The emotional connotations stored for each term in the database are associated with either a positive emotional category or a negative emotional category. Thus, the global count can be divided to rate the passage for positive emotions and for negative emotions. Referring again to

FIG. 5

a bar graph

70

is displayed indicating the positive emotion rating of the passage. Further, the global count can be broken down more finely by emotional category.

FIG. 5

shows the negative emotions separated into the categories of negative emotions with a bar graph

71

displayed for each category.

In a preferred embodiment, a category by category analysis is performed to obtain the ratings for positive and negative emotions. For a given category, the level

1

rating for connotative content is derived from the following equation:

E

ip

=

∑

j

=

1

n

⁢

D

i

,

j

n

where

E

ip

=Rating of connotative content of a passage, p, for emotional category i;

n

p

=total number of words having connotative content in passage p (i.e., excludes articles, conjunctions, forms of the verb “to be”, et cet.); and

D

i,j,p

=intensity of word j in passage p for primary emotional descriptor in emotional category i.

The total rating for all positive emotions is the average rating for the four positive emotional categories. Similarly the total rating for all negative emotions is the average rating for the four negative emotional categories. The total rating for global emotion is the average rating for the four positive emotional categories plus the average rating for the four negative emotional categories. The ratings can be presented in summary fashion (e.g., as for the

FIG. 5

examples of global emotion and positive emotion) or by category (e.g., as for the

FIG. 5

examples of negative emotion). In some embodiments a different rating scale is used for the judge's selections (e.g., 0 to 7) than for the displayed intensities (e.g., 0 to 10). The ratings in

FIG. 5

are shown to be the same 0-10 rating as for the displayed intensities. Thus, to achieve a rating along such scale, the rating is converted to a 0 to 10 scale before being displayed.

With regard to the human interest rating, the passage is analyzed in multiple ways. One way is to count the number of terms which identify people (i.e., terms which are names of people, personal pronouns, references by gender (man, woman, grandma), and non-gender references to people (clown, firefighter).

FIG. 5

shows a rating for human interest by words. Such rating is derived from the proportion of words which relate to people to the total number of words in the passage, placed on a scale of 0 to 10.

Another human interest rating is by sentence. Each sentence which includes quoted dialogue, a question, an exclamation, or a command, each incomplete sentence, and each sentence addressed directly to the audience (e.g., reader) are considered to be conversational in nature. The proportion of such conversational sentences to the total number of sentences in the passage is displayed along a scale of 0 to 10, or another scale. Such rating is used as the human interest rating by sentence.

Still another method for rating human interest is a global rating. The global human interest rating is derived from the human interest rating for words and the human interest rating for sentences. The rating for words is weighted more heavily than the rating for sentences. The specific weighting factor may vary. In one embodiment the rating for words, on a scale of 0 to 10, is multiplied by 3.75, while the rating for sentences on a scale of 0 to 10, is multiplied by 0.3. The two weighted ratings then are summed and divided by 2 to get a weighted average. Such weighted average is displayed. In

FIG. 5

the global human interest rating is displayed along a scale of 0 to 10, although the scale may vary.

With regard to ‘power’, the passage is analyzed by deriving the average power rating for all the terms in the passage. In one embodiment only those terms having a power rating are included in the derivation of the average. In another embodiment, a word without an entry in the database is considered to have a power rating of 0. For terms of the passage which correspond to a record in the database, the power rating is read from the corresponding field of the record. The average power rating stored in one embodiment is along a scale of 0 to 7, where each term in the data base

12

has a field for storing the power rating of such term along the same scale. Note however that different scales may be used for displaying data than for rating the data by evaluators. For example, it may be more meaningful for an evaluator to use a scale of 0-5 to rate power. However, it may be more beneficial to display the same rating on a scale of 0-10, for either consistency among sales or for easier acceptance to a user. Thus, the derived power rating can be rescaled to comply with a display scale which differs from the data base scale for power. A similar method is used to derive an average ‘activity’ rating for the passage and a rating of ‘abstractness versus concreteness’ for the passage. In particular each record in the database may include a field which stores an activity rating for the term associated with such record. An activity rating is the degree to which the term is an action of relates to an action or activity. Each record in the database also may store a field which associates a term with a relative degree of abstractness or concreteness, where abstract is at an opposite end of a scale from concrete.

FIG. 5

shows an example of a power rating, activity rating and abstract/concrete rating for the passage

54

using bar graphs

72

,

73

,

74

. Of course other visual indicators may be used to convey the respective ratings.

Referring to

FIG. 6

, a more detailed level

2

analysis of the connotative content of the passage

54

is provided by clicking on the level

2

tab

64

. The level

2

information includes a listing of the most frequent (i.e., dominant) emotional connotations. In various embodiments such listing can be according to a global ranking for all emotional descriptors. Alternatively, the listing can be according to a ranking by positive emotions in one list and negative emotion in another list. The listing can be more precisely defined into a list for each emotional category.

FIG. 6

shows one rank-ordered list of the most frequent positive emotional descriptors associated with the terms of the passage.

FIG. 6

shows a set of rank-ordered lists of the most frequent emotional descriptors according to negative emotional categories. Lists for 2 negative emotional categories are shown, although lists for the other two negative emotional categories could be viewed by scrolling down in the window area

57

.

The terms of the passage associated with the rank-ordered emotional descriptors are shown in parentheses adjacent to the emotional descriptor in the example illustrated. The number of descriptors listed, the inclusion of passage terms and the method of ordering the descriptors may vary.

Referring to

FIGS. 7 and 8

, an even more detailed level

3

analysis of the connotative content of the passage

54

is provided by clicking on the level

3

tab

66

. The level

3

analysis identifies the dominant words in the passage

54

for emotional connotation, human interest, concreteness or abstractness, power and activity. To derive a dominant word, each word achieves a score based on the number of occurrences of the word (in the given context—so that it identifies a unique record in the database) times the intensity rating for such word. With regard to dominant words for emotional connotation, it is the average intensity among all emotional connotations for such word that is used. For example, consider a first word with intensity ratings of 1, 2, 3, 4, 3, 2, 1, 0 for the eight emotional categories. If such word occurs 10 times in the passage, then it receives a score of 20. The average intensity is 16/8 =2. The score then is 2*10 =20. Consider a second word that has intensity ratings of 3, 4, 3, 4, 3, 4, 3, 4 for the eight emotional categories. The average intensity rating is 3.5. If such word occurs 5 times in the passage, then it receives a score of 17.5. Thus, the first word is more dominant in emotional connotation than the second word, even though the average intensity associated with the first word is less. The number of occurrences causes the first word to be more dominant in this example. Consider a third word that has intensity ratings of 9, 9, 9, 9, 9, 9, 9, 9 for the eight emotional categories. The average intensity rating is 9. If such word occurs 3 times in the passage, then it receives a score of 27. Thus, the third word is more dominant in emotional connotation than the first word or the second word, even though the number of occurrences is less than for either the first word or the second word. The average intensity rating causes the third word to be more dominant in this example.

A similar approach is implemented to rank the words for dominance in the passage based on power, activity and concreteness. For these ranking however there is only one rating stored for each word for the specific ranking. Thus we do not need to calculate an average. The most dominant word is the word with the highest score, where the score is based on the number of occurrence times the ranking. Thus, for a power ranking, the score is the number of occurrences of a word in the passage times the power rating for such word.

To rank the dominant words in the area of human interest, all the words that identify people are ranked by number of occurrences. Such words include names of people, personal pronouns, references by gender (man, woman, grandma), and nongender references to people (clown, firefighter).

In addition a rating of the percent of words in the passage that have connotative meaning is derived. There may be some words in the passage which do not have any connotative meaning and others that do have connotative meaning. The percentage of words that do have connotative meaning is a useful indicator for a writer to determine how dramatic or interesting a work is as distinct from being dull or mildly interesting.

Another rating shown in

FIG. 8

is a rating of the percent of words in the passage that are Hayakawa words. Hayakawa words are a set of core words for any given language, as determined by the Hayakawa method known by those of ordinary skill in the art of linguistics. The percentage of words that are Hayakawa words relative to the total number of words in the passage is the displayed Hayakawa rating.

Referring again to

FIG. 5

, summary and recommendation messages are displayed in area

55

of window

52

. Such messages are part of a support database of writing prescriptions and summary information messages. The different prescriptions and messages are displayed when a corresponding trigger occurs. Such corresponding triggers are numerical data from the connotative database. Various ratings of positive and negative emotions result in corresponding messages. In

FIG. 5

, the mix of positive and negative emotions results in a message “Your text communicates a good mix of positive and negative emotional connotations.” Another message would say a substantial mix rather than good mix for a higher rating of positive and negative emotional categories. For a lower rating the message may say a poor mix or even indicate that the writing does lacks connotative value. Similarly messages are present relating to human interest rankings, power ranking, and abstractness or concreteness. For example, messages are included which recite that the writing is abstract, or is somewhat abstract, that the writing conveys a high level of human interest or is devoid of human interest.

Recommendation messages also are displayed in area

55

. Such messages are triggered by similar profiles of various emotional, human interest, power and abstract ratings. Exemplary recommendations included in the support data base in one embodiment, include:

All sentences are declarative. for more human interest consider recasting some of your material in the form of questions, commands or dialogue.

Consider using more examples or illustrations incorporating concrete nouns. This will reduce abstractness and increase connotative values.

Do not distribute this text.

Rewrite this text from scratch.

In addition, other messages are included. The tone and content of the messages may vary for differing embodiments.

In a best mode embodiment the data base is stored on a computer readable storage media, such as embedded memory, RAM, ROM, a hard disk, an optical disk, a floppy disk, magneto-optical disk, electro-optical disk, or another known or to be implemented transportable or non-transportable computer readable storage media. The user interface, processing filters and other executable instruction code for maintaining and accessing the data base also is stored on the same or another computer readable storage media of the types listed above.

Under various computing models, the connotative language analysis system

10

is installed at an end user computer or accessed remotely from an end user computer. For resident computing models, the executable instructions may be loaded onto the computer and the data base accessed from a transportable storage media. Alternatively the data base also may be installed locally. One skilled in the art will appreciate the many computing configurations that may be implemented. For non-resident computing models, the data base may be stored at a server computer on a public or private, local or wide area network, or even on a global computer network. The executable instructions may be run either at the end user computer or at a server computer with the data being displayed at the end user's display device.

Identifying Connotative Meaning

According to a preferred embodiment of the invention the connotative meanings associated with the terms found in the connotative language reference system

10

are derived by subjective responses from a plurality of evaluators. In a best mode embodiment the evaluators are a panel of persons having objective credentials or accepted expertise in connotative analysis. However, in some embodiments the evaluators may be selected at random. Such persons are referred to herein as connotative judges. In a preferred embodiment, the Internet is used as a recruitment medium to recruit 100 to 200 individuals who are not known to each other to act as independent connotative judges. In one embodiment, the connotative judges are screened for the following characteristics listed below in Table 4:

TABLE 4

Characteristics and Qualifications of Connotative Judges

1. 25% of all judges aged 40 or older and female

2. 25% of all judges under the age of 40 and female

3. 25% of all judges aged 40 or older and male

4. 25% of all judges under the age of 40 and male

5. All judges having at least 2 years of post-secondary education

6. All judges having an above-average vocabulary and command of whichever language is being used to practice the invention.

7. All judges having a substantial interest and some experience in the craft of writing, preferably creative writing.

8. Judges geographically dispersed over the area of interest for the language of interest.

While the above qualifications are used in one embodiment, the invention may be practiced using any number of judges having any qualifications of one's choosing. For example, connotative judges may be only women, or only men, or only individuals of a defined age or ethnic group, or only people who reside in a certain geographical location. The nature and quality of data captured will of course vary with the demographic profile of connotative judges, as well as with the number of judges used when practicing the invention, their geographical locations, and the linguistic qualifications of the judges.

The connotative judges evaluate the meaning of given words and phrases for connotative content using a questionnaire. The questionnaires preferably are distributed as database software files, although they may also be distributed in paper document form. The responses of the connotative judges are processed using either custom-designed database software or database software commercially available from manufacturers such as Inprise, Oracle, and Microsoft. As the data are analyzed, a database of connotative meaning is constructed, which is linked with each context of each word in the connotative language reference system. Each questionnaire is, in effect, a small database table containing four data fields, preferably five data fields, as summarized in Table 5.

TABLE 5

Data Fields for Questionnaire Tables to Capture Connotative Data

Field 1

A field containing a term selected at random from the term

field of the main database

Field 2

A field containing the denotative context for the term in Field 1

Field 3

A field containing the part of speech for the term in Field 1

(optional, but preferred)

Field 4

A blank field assigned for the connotative judge to record data

identifying emotional connotations associated with the term and

context in Fields 1 and 2

Field 5

A blank field assigned for the connotative judge to record data

corresponding to intensity of emotion associated with the term,

context and part of speech in Fields 1, 2 and 3

Field

4

is used for identifying connotational meaning. Field

5

is used for identifying the intensity of the connotation. The use of Field

5

is described below in the section, Quantifying Intensity of Connotative Meanings.

In one embodiment, a distributed computing model is employed, in which the connotative judges use their own computers in their own homes or offices to receive questionnaire tables over the Internet (via e-mail or from a World Wide Web site) that are extracted from the main database

12

. The connotative judges complete their work on the questionnaire tables, and then return the data tables over the Internet.

Referring to

FIG. 9

, a pool

80

of connotative judges are recruited to evaluate records of the connotative data base

12

for connotative associations of corresponding words and phrases. A sample of judges from the pool

80

forms a panel

82

used to evaluate a set of records. The same or different panels are formed to evaluate other sets of records. The number of records in a set may vary. For purposes of illustration a panel of 24 judges is described which evaluates a set of 500 records. In a preferred embodiment, each judge typically receives a questionnaire table

84

covering approximately 500 records, each record consisting of the four fields identified in Table 4. The questionnaire also includes instructions for selecting a code number to fill in the blank Field

4

for each record. Each connotative judge is also supplied with one or more of the eight category lists of code-numbered emotional descriptors identified in Table 2. In a preferred embodiment for a given questionnaire each judge is supplied with only one of the eight category lists of emotional descriptors found in Table 2. Thus, in a given questionnaire a judge evaluates the terms for connotative meaning in only a specific emotional category. To complete the questionnaire table, the connotative judge inputs one of three codes or code types for each record in Field

4

, as listed in Table 6.

TABLE 6

Coding Alternatives for Capturing SET

2

Connotative Data

1. Code the two-digit number (see Table 2 above) associated with one and only one emotional descriptor that most closely matches the emotional connotation that the connotative judge associates with the word or phrase, considering the denotative context and part of speech.

2. Code “00” if the connotative judge understands the word or phrase and its denotative context, but does not associate any of the emotional descriptors from the supplied list of emotional descriptors with the word or phrase and its denotative context.

3. Code “99” if the connotative judge does not know the word or phrase, or the specific associated denotative context.

In a preferred method of practicing the invention, only one judgment is required for each record in a questionnaire table. However, in other embodiments more than one judgment may be allowed or required, if, for example, one wishes to capture the connotative judge's first choice and also the connotative judge's second choice of emotional descriptor. To effect such data capture, the number of connotative fields

46

(see

FIG. 2

) would need to be expanded accordingly, and the questionnaire table structure modified to include additional data capture fields.

The connotative judge repeats this procedure for all 500 records in the questionnaire, then returns the completed questionnaire table

84

via the Internet. Thereafter, the judge may receive another questionnaire table

86

, or

88

to evaluate. The next questionnaire table received by the connotative judge may contain exactly the same set of records that was just evaluated, but accompanied by a different category list of emotional descriptors to be used for coding. Alternatively, the next questionnaire table may contain a completely different selection of records. The exchange of questionnaire tables continues iteratively for the duration of connotative data collection.

In a preferred embodiment of the invention, each block of 500 records is evaluated in this manner eight times (corresponding to the eight category lists of connotative descriptors listed in Table 2), each time by 24 different connotative judges selected at random from the pool of 100 to 200 available connotative judges, using a judge-selection technique that stratifies sampling to ensure equal representation according to the guidelines summarized in Table

4

. Note that the number of judges selected, the size of the pool and the number of records processed in a given questionnaire may vary.

Typically a plurality of panels

82

,

83

are formed to evaluate the database

12

records for connotative associations. Different panels

82

,

83

receive either the same or different questionnaires

84

-

89

. For the exemplary embodiment where 24 judges evaluate each of 500 records in a given questionnaire, the same 24 judges may or may not evaluate all eight categories of emotional connotations for such 500 records.

Quantifying Intensity of Connotative Meanings

As previously described each judge receives a questionnaire. In one embodiment the questionnaire is in table format. Each record in the table has multiple fields as listed above in Table 5. Fields

1

,

2

and

3

are already complete and correspond to the term, a denotative context for such term, and a part of speech. Field

4

is filled in as described above to identify connotative meaning for the word or phrase. Field

5

is to be filled in to record the intensity associated with the connotative meaning provided in Field

4

. When a judge indicates that there is no connotative meaning for the word or phrase or that the judge does not know the term or the specific denotative context, then there is no need to quantify an intensity in Field

5

. Where a judge is permitted to provide two connotative meanings (e.g., a primary and a secondary connotative meaning) then additional fields are included for each record in the questionnaire (e.g., two fields corresponding to Field

4

and two fields corresponding to Field

5

).

Typically a judge quantifies the intensity of the connotative meaning when selecting the connotative meaning itself. The intensity is a scaled value judgement of the judge. Table 7 lists the ratings scale for one embodiment.

TABLE 7

General Coding Model for Capturing Field 5 Data

1

2

3

4

5

6

7

slightly

very

intense

intense

0=automatically coded, corresponding to ‘00’ code entry in Field

4

9=automatically coded, corresponding to ‘99’ code entry in Field

4

In a preferred embodiment, a guiding set of anchor terms are included which are prerated for intensity. The judge's review of such guiding set improves consistency and accuracy among many judges completing the questionnaires. Specifically, the anchor terms are terms that are associated with scale numbers that represent the average intensity scores that other people have provided for various terms. Each connotative judge is expected to disagree with some of the scores represented by some of the anchor terms. For this reason, connotative judges are instructed to either, (i) highlight only those anchor terms with which they are comfortable, or (ii) alternatively, cross out those anchor terms with which they are uncomfortable. The connotative judge then compares the term he or she is scaling in the questionnaire table with his or her intensity of feeling associated with the anchored scales, choosing the scale number with the closest match.

The anchor terms are representative averages; they do not imply “correctness,” but rather provide the connotative judge with indicators of the relative strength of feeling that the connotative judge associates with the chosen emotional descriptor that is meant to be associated with each score number. In effect the anchor terms serve as a way for the judges to calibrate themselves to a scale of intensity. Table 8 presents a typical list of anchor terms associated with a group of emotional descriptors subsumed under the emotional category ‘Sadness,’ which is one of the eight emotional categories previously described.

TABLE 8

Anchor Terms for Scaling Intensity of Connotative Feeling for the Emotional Category“Sadness”

(NOTE: the numbers 1 through 12 for the anchor words are merely for reference purposes. NO RANK ORDER is implied by the numbering within a scale level.)

SCALE LEVEL 7 (Very Intense Sadness)

1. Holocaust—n the mass murder by the Nazis of the Jews of continental Europe between 1940 and 1945.

2. Auschwitz—n Polish town, site of a Nazi death camp during World War II.

3. child abuse n physical, sexual, or emotional ill-treatment or neglect of a child by parents or other adults.

4. AIDS—n acquired immune (or immuno-) deficiency syndrome.

5. rape victim—n a person who has suffered rape/sexual attack

6. starving person—n a person whose health is deteriorating from lack of food

7. murder—n the unlawful premeditated killing of one human being by another

8. cancer—n malignant growth or tumor; uncontrolled cell division

9. suicide—n the act or an instance of killing oneself intentionally

10. death—n the permanent end of all functions of life

11. blind—adj unable to see; sightless

12. insane—adj mentally deranged; crazy; of unsound mind

SCALE LEVEL 6