1 / 27

Sentiment Analysis Based on Chinese Thinking Modes

Sentiment Analysis Based on Chinese Thinking Modes. Yang Liang. Introduction Thinking models Description of Chinese Sentiment Expression Model Implicit Chinese Sentiment Expression Mining Based on LSA Experiment Setting and Evaluation. Outline.

kolton
Download Presentation

Sentiment Analysis Based on Chinese Thinking Modes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Sentiment Analysis Based on Chinese Thinking Modes Yang Liang

  2. Introduction Thinking models Description of Chinese Sentiment Expression Model Implicit Chinese Sentiment Expression Mining Based on LSA Experiment Setting and Evaluation Outline

  3. Sentiment analysis ——a popular research topic in NLP in recent years Blog ,twitter, comment Sentiment analysis in China phase level, sentence level(COAE) Less work in passage level Our work relationships between thinking modes and language Chinese sentiment expression model and LSA Introduction

  4. Introduction Thinking models Description of Chinese Sentiment Expression Model Implicit Chinese Sentiment Expression Mining Based on LSA Experiment Setting and Evaluation Outline

  5. Spiral Graphic Mode and Straight Line Mode Concreteness and Abstractness Scatter view and Focus view Thinking models

  6. Spiral Graphic Mode reflects in the passage organization the topic of the passage is discussed after examples Straight Line Mode focus on deduction and thinking in a straight line way tend to state their views directly and frankly Spiral Graphic Mode and Straight Line Mode

  7. Example (1) Chinese: “他被眼前的一幕震惊了。” English: “He was shocked by what he saw.” (2) Chinese: “经过反复的思考,我终于得到了完美的答案。” English: “I got a perfect answer after deeply thinking.” Spiral Graphic Mode and Straight Line Mode

  8. Spiral Graphic Mode and Straight Line Mode Concreteness and Abstractness Scatter view and Focus view Thinking models

  9. Concreteness Chinese uses quantities of specific words, shapes, sounds and description to illustrate abstract things Abstractness English tend to implement general vocabularies and their variants to express abstract feelings or opinions, such as “-ion”, “-ance” and “-ness” Concreteness and Abstractness

  10. Example (1) Chinese: 土崩瓦解。 English: Disintegration (2) Chinese: 有志者,事竟成 English: When there is a will , there is a way. Concreteness and Abstractness

  11. Spiral Graphic Mode and Straight Line Mode Concreteness and Abstractness Scatter view and Focus view Thinking models

  12. Scatter View Chinese tend to emphasize unified whole Example : more than one verb is used in one Chinese sentence Focus View English pay more attention to logical reasoning or deduction express their feelings or emotions briefly thinking Scatter View and Focus View

  13. Example (1) Chinese: 他拿着课本走进了教室。 English: He walked into the classroom with a textbook in hands. (2) Chinese: 他们俩青梅竹马,两小无猜 English: The boy and the girl were playmates in their childhood. Scatter View and Focus View

  14. Introduction Thinking models Description of Chinese Sentiment Expression Model Implicit Chinese Sentiment Expression Mining Based on LSA Experiment Setting and Evaluation Outline

  15. Quantification of “Spiral Graphic Mode” emotion-determining words mostly locate the end part of Chinese sentences the closer ai locates the tail, the larger the score(ai) will be Description of CSE Model

  16. Quantification of “Concreteness” Chinese sentiment expression, verb also plays an important role,“脸红”“溃败” the highest priority to adjective, then the higher priority is given to the verb, finally other words are processed Description of CSE Model

  17. Quantification of “Scatter View” View window to simulate the Scatter View fixed at 6 Extend Resource CRF,COAE Description of CSE Model

  18. Quantification of Similarities between Thinking Modes The similarities between Chinese and English Example: 1) Chinese: “这个酒店什么都好,就是服务让人失望。” English: “Every aspect about the hotel is ok except the disappointing service.” 2) 脸红,blush Description of CSE Model

  19. Introduction Thinking models Description of Chinese Sentiment Expression Model Implicit Chinese Sentiment Expression Mining Based on LSA Experiment Setting and Evaluation Outline

  20. The Criterion of Implicit Emotion Sample Emotion expressed in an indirect way the implicit emotion articles ,low scores group of samples are chosen to determine the threshold,(from DUTIR Emotion Ontology ) Implicit Emotion Classification Based on LSA LSA, relationships between the implicit samples and the seed samples Implicit Chinese Sentiment Expression Mining Based on LSA

  21. Introduction Thinking models Description of Chinese Sentiment Expression Model Implicit Chinese Sentiment Expression Mining Based on LSA Experiment Setting and Evaluation Outline

  22. Experiments of Chinese Thinking modes in different domains Corpus , three domains, 4000 hotel reviews, 1608 electronics reviews and 1047 stock reviews result Experiment Setting and Evaluation

  23. Elec adding each Chinese thinking mode the precision has increased. elec-pos reviews is not increasing obviously——too much noun Experiment Setting and Evaluation

  24. Stock stock-neg data set is not good great number of specialized words exist and part of them does not appear in DUTIR emotion ontology Experiment Setting and Evaluation

  25. Experiment of Chinese sentiment expression model and LSA Experiment results on ChnSentiCorp Statistic data of implicit samples Experiment Setting and Evaluation

  26. Secondary classification by implementing LSA Macro-Average-Precision of different methods in ChnSen Experiment Setting and Evaluation

  27. Thanks for your attention

More Related