METHOD AND ELECTRONIC DEVICE FOR SENTIMENT CLASSIFICATION

• US 20170169008A1
• Filed: 08/19/2016
• Published: 06/15/2017
• Est. Priority Date: 12/15/2015
• Status: Abandoned Application

• Current Assignee: Le Holdings (Beijing) Co., Ltd., Le Shi Internet Information Technology Corp. Beijing
• Original Assignee: Le Holdings (Beijing) Co., Ltd., Le Shi Internet Information Technology Corp. Beijing
• Inventors: KANG, Chaoming
• Application Number:
  US15/241,994
• Publication Number:
  US 20170169008A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 40/242 Dictionaries
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

Embodiments of the present disclosure provide a method and device for emotion classification method. The method comprises: obtaining a plurality of keywords in a document to be processed; looking up at least one associated word associated with each of the keywords according to a preset association mode; determining emotion category of each of the keywords and the associated words using a preset emotion dictionary; counting the number of words corresponding to each of the emotion categories; and determining the emotion category with the largest number of words as the emotion category of the document to be processed.

Claims

1. A method for emotion classification, comprising at an electronic device:

obtaining a plurality of keywords in a document to be processed;

looking up at least one associated word associated with each of the keywords according to a preset association mode;

determining emotion category of each of the keywords and the associated words using a preset emotion dictionary;

counting the number of words corresponding to each of the emotion categories; and

determining the emotion category with the largest number of words as the emotion category of the document to be processed.

2. The method for emotion classification according to claim 1, wherein, the looking up at least one associated word associated with each of the keywords according to the preset association mode comprises:

obtaining parts-of-speech of all words in the document to be processed;

deleting words having a preset part-of-speech and words in a preset blacklist;

judging whether there are word pairs satisfying an association rule in words obtained after the deleting;

judging whether there are word pairs containing any one of the keywords, when there are the word pairs satisfying the association rule; and

determining the word except the keyword in each of the word pairs containing any one of the keywords as the associated word associated with the keyword in the word pair, when there are the word pairs containing any one of the keywords.

3. The method for emotion classification according to claim 1, further comprising:

converting a plurality of training documents into a target format;

training a word vector model using the training documents of the target format;

obtaining a preset number of seed words belonging to different emotion categories;

calculating similar words belonging to the different emotion categories by the word vector model, according to the seed words of the different emotion categories;

selecting a preset number of similar words with highest similarity as candidate words belonging to the different emotion categories; and

constructing the emotion dictionary according to all of the candidate words belonging to the different emotion categories.

4. The method for emotion classification according to claim 1, wherein, the obtaining the plurality of keywords in the document to be processed comprises:

obtaining keywords with importance degrees greater than a preset importance degree in the document to be processed;

or obtaining keywords input by a user.

5. The method for emotion classification according to claim 4, wherein, the obtaining keywords with importance degrees greater than the preset importance degree in the document to be processed comprises:

deleting words with a preset part-of-speech and words in a preset blacklist in the document to be processed;

calculating term frequency for each of the words;

calculating inverse document frequency for each of the words; and

determining the importance degree of each of the words in the document to be processed based on the term frequency and the inverse document frequency corresponding to the word.

6. A non-volatile computer-readable storage medium, which is stored with computer executable instructions that, when executed by an electronic device, cause the electronic device to:

obtain a plurality of keywords in a document to be processed;

look up at least one associated word associated with each of the keywords according to a preset association mode;

determine emotion category of each of the keywords and the associated words using a preset emotion dictionary;

count the number of words corresponding to each of the emotion categories; and

determine the emotion category with the largest number of words as the emotion category of the document to be processed.

7. The non-volatile computer-readable storage medium according to claim 6, wherein, the looking up at least one associated word associated with each of the keywords according to the preset association mode comprises:

obtaining parts-of-speech of all words in the document to be processed;

deleting words having a preset part-of-speech and words in a preset blacklist;

judging whether there are word pairs satisfying an association rule in words obtained after the deleting;

judging whether there are word pairs containing any one of the keywords, when there are the word pairs satisfying the association rule; and

determining the word except the keyword in each of the word pairs containing any one of the keywords as the associated word associated with the keyword in the word pair, when there are the word pairs containing any one of the keywords.

8. The non-volatile computer-readable storage medium according to claim 6, wherein, the execution of the computer executable instructions further causes the electronic device to:

convert a plurality of training documents into a target format;

train a word vector model using the training documents of the target format;

obtain a preset number of seed words belonging to different emotion categories;

calculate similar words belonging to the different emotion categories by the word vector model, according to the seed words of the different emotion categories;

select a preset number of similar words with highest similarity as candidate words belonging to the different emotion categories; and

construct the emotion dictionary according to all of the candidate words belonging to the different emotion categories.

9. The non-volatile computer-readable storage medium according to claim 6, wherein, the obtaining the plurality of keywords in the document to be processed comprises:

obtaining keywords with importance degrees greater than a preset importance degree in the document to be processed;

or

obtaining keywords input by a user.

10. The non-volatile computer-readable storage medium according to claim 9, wherein, the obtaining keywords with importance degrees greater than the preset importance degree in the document to be processed comprises:

deleting words with a preset part-of-speech and words in a preset blacklist in the document to be processed;

calculating term frequency for each of the words;

calculating inverse document frequency for each of the words; and

determining the importance degree of each of the words in the document to be processed based on the term frequency and the inverse document frequency corresponding to the word.

11. An electronic device, comprising:

at least one processor; and

a memory, communicably connected with the at least one processor and storing instructions executable by the at least one processor,

wherein execution of the instructions by the at least one processor causes the at least one processor to;

obtaining a plurality of keywords in a document to be processed;

looking up at least one associated word associated with each of the keywords according to a preset association mode;

determining emotion category of each of the keywords and the associated words using a preset emotion dictionary;

counting the number of words corresponding to each of the emotion categories; and

determining the emotion category with the largest number of words as the emotion category of the document to be processed.

12. The electronic device according to claim 11, wherein, the looking up at least one associated word associated with each of the keywords according to the preset association mode comprises:

obtaining parts-of-speech of all words in the document to be processed;

deleting words having a preset part-of-speech and words in a preset blacklist;

judging whether there are word pairs satisfying an association rule in words obtained after the deleting;

judging whether there are word pairs containing any one of the keywords, when there are the word pairs satisfying the association rule; and

determining the word except the keyword in each of the word pairs containing any one of the keywords as the associated word associated with the keyword in the word pair, when there are the word pairs containing any one of the keywords.

13. The electronic device according to claim 11, wherein, the execution of the instructions by the at least one processor further causes the at least one processor to:

;

convert a plurality of training documents into a target format;

train a word vector model using the training documents of the target format;

obtain a preset number of seed words belonging to different emotion categories;

calculate similar words belonging to the different emotion categories by the word vector model, according to the seed words of the different emotion categories;

select a preset number of similar words with highest similarity as candidate words belonging to the different emotion categories; and

construct the emotion dictionary according to all of the candidate words belonging to the different emotion categories.

14. The electronic device according to claim 11, wherein, the obtaining the plurality of keywords in the document to be processed comprises:

obtaining keywords with importance degrees greater than a preset importance degree in the document to be processed;

or

obtaining keywords input by a user.

15. The electronic device according to claim 14, wherein, the obtaining keywords with importance degrees greater than the preset importance degree in the document to be processed comprises:

deleting words with a preset part-of-speech and words in a preset blacklist in the document to be processed;

calculating term frequency for each of the words;

calculating inverse document frequency for each of the words; and

determining the importance degree of each of the words in the document to be processed based on the term frequency and the inverse document frequency corresponding to the word.