Semantic similarity for aspect-based sentiment analysis
Abstract
The article investigates the problem of aspect-based sentiment analysis. Such version of analysis is more challenging compared to general task of sentiment detection problem. It implies the solutions to the number of related subtasks such as aspect term extraction, aspect term polarity detection and aspect category polarity detection. The solution of aspect-based sentiment analysis problem significantly extends the capabilities of natural language processing systems.
The article gives the overview of previous works in the field and describes the train and test data from the Russian evaluation workshop SentiRuEval. For the task of aspect term extraction the vector space of distributed representations of words was used. Aspect term detection is based on mutual information method and semantic similarity. The paper contains the number of experimental results. At the end the final conclusions are drawn.
About the Authors
Е. Котельников
Вятский государственный гуманитарный университет
Russian Federation
Russian Federation
П. Блинов
Вятский государственный гуманитарный университет
Russian Federation
Russian Federation
References
1. Feldman R. Techniques and applications for sentiment analysis // Communications of the ACM. 2013. V. 56. P. 82- 89.
2. Liu B. Sentiment analysis and opinion mining // Synthesis Lectures on Human Language Technologies. 2012. V. 5.
3. Blinov P.D., Kotelnikov E.V. Using distributed representations for aspect-based sentiment analysis // Proceedings of International Conference Dialog. 2014. Issue 13 (20). P. 64-75.
4. Pontiki M., Galanis D., Pavlopoulos J., Papageorgiou H., Androutsopoulos I., Manandhar S. SemEval-2014 Task 4: Aspect Based Sentiment Analysis // Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 2014. P. 27-35.
5. Hu M., Liu B. Mining and summarizing customer reviews // Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004. P. 168-177.
6. Schouten K., Frasincar F., Jong F. COMMIT-P1WP3: A Co-occurrence based approach to aspect-level sentiment analysis // Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 2014. P. 203-207.
7. Pekar V., Afzal N., Bohnet B. UBham: lexical resources and dependency parsing for aspect-based sentiment analysis // Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 2014. P. 683-687.
8. Zhang F., Zhang Z., Lan M. ECNU: A combination method and multiple features for aspect extraction and sentiment polarity classification // Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 2014. P. 252-258.
9. Kiritchenko S., Zhu X., Cherry C., Mohammad S. NRC-Canada-2014: Detecting aspects and sentiment in customer reviews // Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 2014. P. 437-442.
10. Chernyshevich M. IHS R&D Belarus: cross-domain extraction of product features using conditional random fields // Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 2014. P. 309-313.
11. Toh Z., Wang W. DLIREC: aspect term extraction and term polarity classification system // Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 2014. P. 235-240.
12. Brun C., Popa D., Roux C. XRCE: hybrid classification for aspect-based sentiment analysis // Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 2014. P. 838-842.
13. Gupta D., Ekbal A. IITP: supervised machine learning for aspect based sentiment analysis // Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 2014. P. 319-323.
14. Bornebusch F., Cancino G., Diepenbeck M., Drechsler R., Djomkam S., Fanseu A., Jalali M., Michael M., Mohsen J., Nitze M., Plump C., Soeken M., Tchambo F., Toni, Ziegler H. iTac: aspect based sentiment analysis using sentiment trees and dictionaries // Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 2014. P. 351-355.
15. Wagner J., Arora P., Cortes S., Barman U., Bogdanova D., Foster J., Tounsi L. DCU: aspect-based polarity classification for SemEval Task 4 // Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 2014. P. 223-229.
16. Loukachevitch N.V., Blinov P.D., Kotelnikov E.V., Rubtsova Yu.V., Ivanov V.V., Tutubalina E. SentiRuEval: testing object-oriented sentiment analysis systems in russian // Proceedings of International Conference Dialog. 2015. P. 2-13.
17. Mikolov T., Sutskever I., Chen K., Corrado G., Dean J. Distributed representations of words and phrases and their compositionality // Proceedings of NIPS. 2013. P. 3111-3119.
18. Gensim – topic modeling library. URL: http://radimrehurek.com/gensim (дата обращения: 10.04.2015).
19. Manning C., Raghavan P., Schütze H. Introduction to information retrieval. Cambridge University Press. New York. 2008.
20. Islam A., Inkpen D. Second order co-occurrence PMI for determining the semantic similarity of words // Proceedings of the International Conference on Language Resources and Evaluation. 2006. P. 1033-1038.
21. Friedman J. Greedy function approximation: a gradient boosting machine // The Annals of Statistics. 2001. V. 29. P. 1189-1232.
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For citations:
, Semantic similarity for aspect-based sentiment analysis. Russian Digital Libraries Journal. 2015;18(3-4):120-137. (In Russ.)
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