METHOD AND SYSTEM RELATING TO SENTIMENT ANALYSIS OF ELECTRONIC CONTENT

• US 20130311485A1
• Filed: 01/30/2013
• Published: 11/21/2013
• Est. Priority Date: 05/15/2012
• Status: Abandoned Application

• Current Assignee: Whyz Technologies Limited
• Original Assignee: Whyz Technologies Limited
• Inventors: Khan, Shahzad
• Application Number:
  US13/754,437
• Publication Number:
  US 20130311485A1
• Time in Patent Office: Days
• US Class Current: 707/748
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/58 Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation

Abstract

Users receive information which must be filtered, processed, analysed, reviewed, consolidated and distributed or acted upon. Prior art tools automatically processing content to assign sentiment to the content are ineffective as essential aspects such as context are not considered. Embodiments of the invention provide automatic contextual based sentiment classification of content in terms of both sentiments expressed and their intensity. Further a content set is analysed to rapidly establish an “at-a-glance” type assessment of the key topics/themes present within the content set and sentimentally annotate each. Importantly embodiments of the invention also provide for a user to establish the basis for the sentiment associated with an item of or set of content, i.e. make it explainable. Further embodiments of the invention provide for the establishment of psychological tone to sentiments where the sentiments and psychological tones to be tuned from the context or domain of the content.

Claims

1. A method comprising:

receiving an item of content;

parsing the item of content with a microprocessor to generate a linguistic annotated item of content with language associations;

retrieving from a term selection rules repository stored upon a memory at least a rule of a plurality of rules;

applying with the microprocessor the at least a rule of the plurality of rules to establish a set of candidate sentiment carrying terms within the linguistic annotated item of content;

querying the set of candidate sentiment carrying terms against a target-domain sentiment lexicon to generate a set of sentiment labeled terms; and

applying to the linguistic annotated item of content a set of sentiment labeling rules established in dependence of at least the set of sentiment labeled terms to generate a sentiment label for the item of content.

2. The method according to claim 1 wherein,

the language associations are at least one of parts of speech, phrasal elements, and grammatical relations associated with terms that form a predetermined portion of the item of content.

3. The method according to claim 1 wherein,

each sentiment labeled term is associated with at least one of a sentiment label and a sentiment intensity.

4. The method according to claim 3 wherein,

the at least one of the sentiment label and the sentiment intensity are employed in the application to the linguistic annotated item of content of the set of sentiment labeling rules.

5. A method comprising:

a) receiving an item of content;

b) receiving upon a microprocessor an indication of a predetermined portion of the item of content to analyze;

c) establishing with the microprocessor a plurality of positive sentiment terms and a plurality of negative sentiment terms;

d) parsing with the microprocessor the predetermined portion of the item of content to count occurrences of a positive sentiment term of the plurality of positive sentiment terms to establish a positive sentiment count;

e) parsing with the microprocessor the predetermined portion of the item of content to count occurrences of a negative sentiment term of the plurality of negative sentiment terms to establish a negative sentiment count; and

f) determining with the microprocessor a sentiment label to associate with the item of content in dependence upon at least one of the occurrences of the positive sentiment term and occurrences of the negative sentiment term.

6. The method according to claim 5 wherein,

each positive sentiment term of the plurality of positive sentiment terms has an associated positive intensity level;

each negative sentiment term of the plurality of negative sentiment terms has an associated negative intensity level.

7. The method according to claim 6 wherein,

counting occurrences of the positive sentiment terms of the plurality of positive sentiment terms is achieved by:

determining a number of occurrences for each positive sentiment term;

multiplying the number of occurrences for each positive sentiment term by its respective intensity level to generate a weighted occurrence count;

summing the resulting weighting occurrence counts for the plurality of positive sentiment counts to generate the positive sentiment count; and

counting occurrences of the negative sentiment terms of the plurality of negative sentiment terms is achieved by;

determining a number of occurrences for each negative sentiment term;

multiplying the number of occurrences for each negative sentiment term by its respective intensity level to generate a weighted occurrence count;

summing the resulting weighting occurrence counts for the plurality of negative sentiment counts to generate the negative sentiment count.

8. The method of claim 5 further comprising;

establishing a number of predetermined portions of the item of content in step (a) and associating with each predetermined portion of the item of content a portion weighting;

steps (b) to (e) are repeated for a number of predetermined portions of the item of content; and

step (f) now comprises multiplying for each predetermined portion of the item of content the positive and negative sentiment counts by the respective portion weighting for that predetermined portion of the item of content to generate portion weighted positive and negative sentiment counts respectively and summing the results for all predetermined portions of the item of content.

9. The method according to claim 5 further comprising;

determining with the microprocessor a domain associated with the item of content in step (a); and

selecting with the microprocessor a sentiment lexicon of a plurality of sentiment lexicons, the selection made in dependence upon at least the domain.

10. The method according to claim 5 wherein,

determining the sentiment label is at least one of:

also dependent upon the imbalance between the counts of occurrences of the positive sentiment term and negative sentiment term; and

selecting a sentiment label that is not one of either the positive sentiment term or negative sentiment term used in establishing the occurrences.

11. The method according to claim 5 wherein,

generating the sentiment label is achieved in dependence upon at least one the difference, the sum, the ratio of the occurrences of the positive sentiment term and occurrences of the negative sentiment term, the positive sentiment term, and the negative sentiment term.

12. The method according to claim 5 wherein,

generating a psychological tone qualification in dependence upon at least one the difference, the sum, the ratio of the occurrences of the positive sentiment term and occurrences of the negative sentiment term, the positive sentiment term, and the negative sentiment term.

13. The method of claim 5 further comprising;

repeating step (d) for each positive sentiment term of the plurality of positive sentiment terms and each negative sentiment term of the plurality of negative sentiment terms; and

step (f) now comprises summing the results for all of the plurality of positive sentiment terms step (f) now comprises with the microprocessor the sentiment label to associate with the item of content in dependence upon at least one of the occurrences of all positive sentiment terms of the plurality of positive sentiment terms and occurrences of all negative sentiment terms of the plurality of the negative sentiment terms.

14. The method according to claim 11 further comprising;

generating a psychological tone qualification in dependence upon at least one of the distribution of occurrences of all positive sentiment terms of the plurality of positive sentiment terms and the distribution of occurrences of all negative sentiment terms of the plurality of the negative sentiment terms.

15. The method according to claim 5 further comprising;

determining with the microprocessor a domain associated with the item of content in step (a); and

determining a sentiment to associate to an item of content, the determination being in dependence upon at least the domain and the sentiment label.

16. A method comprising:

receiving with an item of content;

processing with a microprocessor the item of content to determine occurrences of content sentiment-carrying terms;

displaying to a user the sentiment labels of content sentiment-carrying terms within the item of content; and

presenting to the user any sentiment intensity variation based on matching at least one of a predetermined sentence and a phrasal syntactic structure of the document with a repository of syntactic structure patterns.

17. The method according to claim 16 wherein,

the sentiment intensity variation is at least one of an increase, a decrease, neutralization and a reversal.

18. The method of claim 16 wherein,

describing any sentiment intensity variation is based upon matching the sentiment of at least two adjacent sentiment-evaluated sentences with the repository of syntactic structure patterns.

19. The method of claim 16 further comprising,

allowing the user to select at least one of the sentiment carrying terms, sentences and rhetorical structures to access an explanation relating to how the derived sentiment label is associated with the clicked entity.

20. A method comprising:

a) receiving a plurality of items of content;

b) identifying with a microprocessor within the plurality of items of content at least a core multi-item concept of a plurality of core multi-item concepts, each core multi-item concept relating to a concept contained at least within a predetermined portion of the plurality of items of concept;

c) selecting a core multi-item concept from the plurality of core multi-item concepts; and

d) establishing with the microprocessor a sentiment relating to the core multi-item concept for the plurality of items of content.

21. The method according to claim 20 wherein,

the sentiment relating to the core multi-item concept for the plurality of content is established by at least one of:

e) determining a count based sentiment for the core multi-item concept for each item of content of the plurality of items of content; and

establishing the sentiment in dependence upon at least the plurality of document count based sentiment; and

f) determining a context count based sentiment by identifying each instance of the core multi-item concept within the plurality of items of content.

22. The method according to claim 20 further comprising:

repeating steps (c) and (d) for a predetermined subset of the plurality of multi-item concepts; and

presenting at least one of the predetermined subset of the plurality of multi-item concepts to the user together with its associated sentiment.

23. The method according to claim 20 further comprising:

e) receiving a second plurality of items of content;

f) repeating steps (c) and (d) for the same core multi-item concept;

g) presenting to a user at least one of;

the original sentiment and a variance established in dependence upon at least the original sentiment and the new sentiment.