TOPIC-SPECIFIC SENTIMENT EXTRACTION

• US 20140358523A1
• Filed: 05/29/2014
• Published: 12/04/2014
• Est. Priority Date: 05/30/2013
• Status: Abandoned Application

• Current Assignee: Wright State University
• Original Assignee: Wright State University
• Inventors: Sheth, Amit P., Wang, Wenbo, Chen, Lu
• Application Number:
  US14/290,436
• Publication Number:
  US 20140358523A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

One or more embodiments of techniques or systems for sentiment extraction are provided herein. From a corpus or group of social media data which includes one or more expressions pertaining to a topic, target topic, or a target, one or more candidate expressions may be extracted. Relationships between one or more pairs of candidate expressions may be identified or evaluated. For example, a consistency relationship or an inconsistency relationship between a pair may be determined. A root word database may include one or more root words which facilitate identification of candidate expressions. Among one or more of the root words may be seed words, which may be associated with a predetermined polarity. To this end, polarities may be determined based on a formulation which assigns polarities to a sentiment expression, candidate expressions, or an expression as a constrained optimization problem.

Claims

1. A method for sentiment extraction, comprising:

receiving one or more expressions, wherein respective expressions comprise a set of one or more words, wherein one or more of the expressions is associated with a target;

extracting one or more candidate expressions from one or more of the expressions, wherein one or more of the candidate expressions comprises a subset of the set of one or more words;

identifying one or more relationships between one or more pairs of candidate expressions from respective expressions and frequencies of respective relationships across one or more of the expressions; and

determining one or more polarities for one or more of the candidate expressions based on one or more positive polarity probabilities for respective candidate expressions, one or more negative polarity probabilities for respective candidate expressions, one or more consistency probabilities for one or more pairs of candidate expressions, one or more inconsistency probabilities for one or more pairs of candidate expressions, and the frequencies of one or more of the relationships between pairs of candidate expressions across one or more of the expressions,

wherein the receiving, the extracting, the identifying, or the determining is implemented via a processing unit.

2. The method of claim 1, wherein one or more of the candidate expressions comprises a root word.

3. The method of claim 2, wherein the root word is sentiment bearing.

4. The method of claim 2, wherein the root word is a seed word associated with a predetermined positive polarity probability and a predetermined negative polarity probability.

5. The method of claim 2, wherein extracting one or more of the candidate expressions is based on a dependency relation between the root word and the target or a proximity between the root word and the target for a corresponding expression.

6. The method of claim 1, wherein extracting one or more of the candidate expressions based on one or more n-grams comprising one or more root words.

7. The method of claim 1, wherein one or more of the relationships is identified as a consistency relation or an inconsistency relation.

8. The method of claim 1, comprising identifying one or more inconsistency relations between a first candidate expression and a second candidate expression based on the first candidate expression comprising a negation and the first candidate expression comprising the second candidate expression.

9. The method of claim 1, comprising identifying one or more inconsistency relations between a first candidate expression and a second candidate expression based on:

an expression comprising the first candidate expression, a contrasting conjunction, and the second candidate expression; and

a lack of negation applied to both the first candidate expression and the second candidate expression.

10. The method of claim 1, comprising identifying one or more consistency relations between a first candidate expression and a second candidate expression based on a lack of negation applied to both the first candidate expression and the second candidate expression.

11. The method of claim 1, wherein one or more of the positive polarity probabilities is indicative of a probability that a corresponding candidate expression is positive.

12. The method of claim 1, wherein one or more of the negative polarity probabilities is indicative of a probability that a corresponding candidate expression is negative.

13. The method of claim 1, wherein one or more of the consistency probabilities is indicative of a probability that a corresponding pair of candidate expressions have the same polarity.

14. The method of claim 1, wherein one or more of the inconsistency probabilities is indicative of a probability that a corresponding pair of candidate expressions have different polarities.

16. The system of claim 11, wherein the parsing component extracts one or more candidate expressions from a corresponding expression by performing sentence splitting on the corresponding expression.

17. The system of claim 16, wherein the parsing component determines one or more dependency relations between one or more of the root words and the target based on the sentence splitting.

18. The system of claim 11, wherein the optimization component minimizes the objective function utilizing an L-BFGS-B algorithm.

19. The system of claim 11, wherein the monitoring component receives one or more of the expressions from one or more social media sources or web sources.

15. A system for sentiment extraction, comprising:

a root word database comprising one or more root words, wherein one or more of the root words are seed words;

a monitoring component receiving one or more expressions, wherein respective expressions comprise a set of one or more words, wherein one or more of the expressions is associated with a target;

a parsing component extracting one or more candidate expressions from one or more of the expressions, wherein one or more of the candidate expressions comprises one or more of the root words;

a relationship component identifying one or more consistency relationships or one or more inconsistency relationships between one or more pairs of candidate expressions from respective expressions and frequencies of respective relationships across one or more of the expressions; and

an optimization component minimizing an objective function associated with one or more polarities for one or more of the candidate expressions based on one or more positive polarity probabilities for respective candidate expressions, one or more negative polarity probabilities for respective candidate expressions, one or more consistency probabilities for one or more pairs of candidate expressions, one or more inconsistency probabilities for one or more pairs of candidate expressions, and the frequencies of one or more of the relationships between pairs of candidate expressions across one or more of the expressions,

wherein the root word database, the monitoring component, the parsing component, the relationship component, or the optimization component is implemented via a processing unit.

20. A computer-readable storage medium comprising computer-executable instructions, which when executed via a processing unit on a computer performs acts, comprising:

receiving one or more expressions, wherein respective expressions comprise a set of one or more words, wherein one or more of the expressions is associated with a target;

extracting one or more candidate expressions from one or more of the expressions, wherein one or more of the candidate expressions comprises a subset of the set of one or more words;

identifying one or more relationships between one or more pairs of candidate expressions from respective expressions and frequencies of respective relationships across one or more of the expressions; and

determining one or more polarities for one or more of the candidate expressions based on one or more positive polarity probabilities for respective candidate expressions, one or more negative polarity probabilities for respective candidate expressions, one or more consistency probabilities for one or more pairs of candidate expressions, one or more inconsistency probabilities for one or more pairs of candidate expressions, and the frequencies of one or more of the relationships between pairs of candidate expressions across one or more of the expressions.