CONTEXTUAL SENTIMENT TEXT ANALYSIS

• US 20150286627A1
• Filed: 04/03/2014
• Published: 10/08/2015
• Est. Priority Date: 04/03/2014
• Status: Abandoned Application

• Current Assignee: Adobe Inc.
• Original Assignee: Adobe Systems Incorporated (Adobe Inc.)
• Inventors: Chang, Walter W., Demiralp, Emre, Kumar, Shantanu, Xia, Shanshan
• Application Number:
  US14/244,665
• Publication Number:
  US 20150286627A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/279 Recognition of textual entities
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

In techniques for contextual sentiment text analysis, a sentiment analysis application is implemented to receive sentences as text data, and each of the sentences can include one or more sentiments about a subject of the sentence. The text data can be received as part-of-speech information that includes noun expressions, verb expressions, and tagged parts-of-speech of the sentences. The sentiment analysis application is implemented to analyze the text data to identify the sentiment about the subject of a sentence, and determine a context of the sentiment as the sentiment pertains to a topic category of the subject in the sentence, where the topic category of the subject is determined based on text categorization of the text data. The sentiment analysis application can also determine whether the sentiment is positive about the subject or negative about the subject based on the context of the sentiment within the topic category of the subject.

Claims

1. A method, comprising:

receiving a sentence as text data that includes a sentiment about a subject of the sentence;

analyzing the text data to identify the sentiment about the subject;

determining a topic category of the subject of the sentence based on text categorization of the text data;

determining a context of the sentiment as the sentiment pertains to the topic category of the subject in the sentence; and

determining whether the sentiment is positive about the subject or negative about the subject based on the context of the sentiment within the topic category of the subject.

2. The method as recited in claim 1, wherein the text data is said received as part-of-speech information that includes one or more of noun expressions, verb expressions, and tagged parts-of-speech of the sentence.

3. The method as recited in claim 2, further comprising:

identifying the noun expressions, the verb expressions, and adjective expressions that are meaningful to the sentiment about the subject, said identifying the noun expressions, the verb expressions, and the adjective expressions from the part-of-speech information.

4. The method as recited in claim 3, further comprising:

determining one or more adjective forms of the adjective expressions utilizing a dictionary database of categorized sentiment vocabulary words to identify sentence phrases that are meaningful to the sentiment about the subject.

5. The method as recited in claim 3, further comprising:

identifying one or more topics of the sentence based on the noun expressions; and

associating each of the one or more topics with the sentiment about the subject.

6. The method as recited in claim 5, further comprising:

aggregating the sentiment about the subject for each of the one or more topics of the sentence to score each of the noun expressions as represented by one of the topics of the sentence.

7. The method as recited in claim 6, further comprising:

determining one or more of positive sentiments about the subject, negative sentiments about the subject, recommendations about the subject, and suggestions about the subject based on the scoring of the topics of the sentence; and

computing a weighted average of sentence sentiment scores to determine an overall sentiment about the subject of the sentence.

8. A computing device, comprising:

a memory configured to maintain text data that is received as one or more sentences;

a processor system to implement a sentiment analysis application that is configured to;

analyze the text data to identify sentiments that are expressed about subjects of the one or more sentences;

determine a context of each of the sentiments as they pertain to topic categories of the subjects in the one or more sentences, the topic categories of the subjects being determinable based on text categorization of the text data; and

determine whether each of the sentiments is positive about a subject or negative about the subject based on the context of each sentiment within the topic category of the subject.

9. The computing device as recited in claim 8, wherein the text data is maintained as part-of-speech information that includes one or more of noun expressions, verb expressions, and tagged parts-of-speech of the sentence.

10. The computing device as recited in claim 9, wherein the sentiment analysis application is configured to identify, based on the part-of-speech information, the noun expressions, the verb expressions, and adjective expressions that are meaningful to each of the sentiments about the subjects.

11. The computing device as recited in claim 10, wherein the sentiment analysis application is configured to determine one or more adjective forms of the adjective expressions utilizing a dictionary database of categorized sentiment vocabulary words to identify sentence phrases that are meaningful to each of the sentiments about the subjects.

12. The computing device as recited in claim 10, wherein the sentiment analysis application is configured to:

identify one or more topics of the one or more sentences based on the noun expressions; and

associate each of the one or more topics with the sentiments about the subjects.

13. The computing device as recited in claim 12, wherein the sentiment analysis application is configured to aggregate the sentiments about the subjects for each of the one or more topics of the one or more sentences to score each of the noun expressions as represented by one of the topics of the sentence.

14. The computing device as recited in claim 13, wherein the sentiment analysis application is configured to:

determine one or more of positive sentiments about the subjects, negative sentiments about the subjects, recommendations about the subjects, and suggestions about the subjects based on the scoring of the topics of the one or more sentences; and

compute a weighted average of sentence sentiment scores to determine an overall sentiment about the subjects of the one or more sentences.

15. A computer-readable storage memory comprising a sentiment analysis application stored as instructions that are executable and, responsive to execution of the instructions by a computing device, the computing device performs operations of the sentiment analysis application comprising to:

receive sentences as text data, each of the sentences including a sentiment about a subject of the sentence, the text data for a sentence including one or more of noun expressions, verb expressions, and tagged parts-of-speech of the sentence;

analyze the text data to identify the sentiment about the subject;

determine a context of the sentiment as the sentiment pertains to a topic category of the subject in the sentence, the topic category of the subject determined based on text categorization of the text data; and

determine whether the sentiment is positive about the subject or negative about the subject based on the context of the sentiment within the topic category of the subject.

16. The computer-readable storage memory as recited in claim 15, wherein the computing device performs operations of the sentiment analysis application further comprising to identify the noun expressions, the verb expressions, and adjective expressions that are meaningful to the sentiment about the subject, said identifying the noun expressions, the verb expressions, and the adjective expressions from the part-of-speech information.

17. The computer-readable storage memory as recited in claim 16, wherein the computing device performs operations of the sentiment analysis application further comprising to determine one or more adjective forms of the adjective expressions utilizing a dictionary database of categorized sentiment vocabulary words to identify sentence phrases that are meaningful to the sentiment about the subject.

18. The computer-readable storage memory as recited in claim 16, wherein the computing device performs operations of the sentiment analysis application further comprising to:

identify one or more topics of the sentence based on the noun expressions; and

associate each of the one or more topics with the sentiment about the subject.

19. The computer-readable storage memory as recited in claim 18, wherein the computing device performs operations of the sentiment analysis application further comprising to aggregate the sentiment about the subject for each of the one or more topics of the sentence to score each of the noun expressions as represented by one of the topics of the sentence.

20. The computer-readable storage memory as recited in claim 19, wherein the computing device performs operations of the sentiment analysis application further comprising to:

determine one or more of positive sentiments about the subject, negative sentiments about the subject, recommendations about the subject, and suggestions about the subject based on the scoring of the topics of the sentence; and

compute a weighted average of sentence sentiment scores to determine an overall sentiment about the subject of the sentence.