Automatic sentiment analysis towards the entity and its characteristics
Main Article Content
Abstract
The paper considers approaches to sentiment analysis towards a specific entity and its characteristics (aspects). To solve the aspect-oriented sentiment analysis task, it is necessary to extract aspect terms from texts, to classify or cluster aspect terms into aspect categories, to determine the sentiment expressed towards the specfic aspect. The paper also briefly presents SentiRuEval-2015 evaluation of aspect-oriented sentiment analysis systems in Russian.
Keywords:
Article Details
References
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13. Feng S., Bose R., Choi Y. Learning general connotation of words using graph-based algorithms // Proceedings of the Conference on Empirical Methods in Natural Language Processing. – Association for Computational Linguistics. 2011. P. 1092-1103.
14. Feng S., Kang J.S., Kuznetsova P., Choi Y. Connotation Lexicon: a dash of sentiment beneath the surface meaning // Proceedings of ACL. 2013. P. 1774-1784.
15. Zhang Lei, Bing Liu. Identifying noun product features that imply opinions // Proceedings of the Annual Meeting of the Association for Computational Linguistics (short paper) (ACL-2011). 2011. P. 575-580.
16. Liu B., Zhang L. A survey of opinion mining and sentiment analysis // Mining Text Data. 2012: Springer US. P. 415-463.
17. Zhang Lei, Liu B. Extracting resource terms for sentiment analysis // Proceedings of IJCNLP-2011. 2011. P.1171-1179.
18. Blair-Goldensohn S., Hannan K., McDonald R., Neylon T., Reis G. A., Reynar J. Building a sentiment summarizer for local service reviews // Proceedings of WWW Workshop on NLP in the Information Explosion Era. 2008.
19. Hu M., Liu B. Mining and summarizing customer reviews // Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2004. P. 168-177.
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21. Moghaddam S., Ester M. Aspect-based opinion mining from online reviews. Tutorial at SIGIR-2012. 2012.
22. Ku Lun-Wei, Yu-Ting Liang, Hsin-Hsi Chen. Opinion extraction, summarization and tracking in news and blog corpora // Proceedings of AAAI-CAAW'06. 2006.
23. Manning C.D., Raghavan P., Schütze H. Introduction to information retrieval. Cambridge: Cambridge University Press, 2008.
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25. Frantzi K., Ananiadou S., Mima H. Automatic recognition of multi-word terms: the C-value/NC-value method // International Journal on Digital Libraries, 2000. V. 3, No 2. P. 115-130.
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27. Hai Z., Chang K., Cong G. One seed to find them all: mining opinion features via association // Proceedings of the 21st ACM international conference on Information and knowledge management. 2012. ACM. P. 255-264.
28. Qiu G., Liu B., Bu J., Chen C. Opinion word expansion and target extraction through double propagation // Computational Linguistics. 2011. V. 1, No 1. P. 1-18.
29. Loukachevitch N., Nokel M. An experimental study of term extraction for real information-retrieval thesauri // Proceedings of Terminology and Artificial Intelligence Conference TIA-2013. 2013. P. 69-78.
30. Zhuang L., Jing F., Zhu X. Movie review mining and summarization // Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2006), 2006. P. 43-50.
31. Zhang L., Liu B., Lim S., O’Brien-Strain E. Extracting and ranking product features in opinion documents // Proceedings of International Conference on Computational Linguistics (COLING-2010). 2010. P. 1462-1470.
32. Kovelamudi S., Ramalingam S., Sood A., Varma V. Domain independent model for product attribute extraction from user reviews using Wikipedia // Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP-2010). 2011. P. 1408-1412.
33. Niklas J., Gurevych I. Extracting opinion targets in a single and cross-domain setting with conditional random fields // Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2010). 2010. P. 1035-1045.
34. Choi Y., Cardie C. Hierarchical sequential learning for extracting opinions and their attributes // Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2010). 2010. P. 269-274.
35. Blei D., Ng A., Jordan M. Latent Dirichlet allocation // The Journal of Machine Learning Research, 2003. No 3. P. 993-1022.
36. Воронцов К.В., Потапенко А.А. Модификации EM-алгоритма для вероятностного тематического моделирования // Машинное обучение и анализ данных. 2013. Т. 1, № 6. C. 657-686.
37. Titov I., McDonald R. A joint model of text and aspect ratings for sentiment summarization // Urbana, 51, 61801. 2008.
38. Zhao Wayne Xin, Jing Jiang, Hongfei Yan, Xiaoming Li. Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid // Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2010). 2010. P. 56-65.
39. Zhai Z., Liu B., Xu H., Jia P. Grouping product features using semi-supervised learning with soft-constraints // Proceedings of Coling-2010. 2010. P. 1272-1280.
40. Zhai Z., Liu B., Xu H., Jia P. Clustering product features for opinion mining // Proceedings of the fourth ACM international conference on Web search and data mining. ACM. 2011. P. 347-354.
41. Yu J., Zha Z.J., Wang M., Wang K., Chua T.S. Domain-assisted product aspect hierarchy generation: towards hierarchical organization of unstructured consumer reviews // Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. 2011. P. 140-150.
42. Andrzejewski D., Zhu X., Craven M. Incorporating domain knowledge into topic modeling via Dirichlet forest priors // Proceedings of ICML. 2009. P. 25-32.
43. Mukherjee A., Liu B. Aspect extraction through semi-supervised modeling // Proceedings of 50th Annual Meeting of Association for Computational Linguistics (ACL-2012). 2012. P. 339-348.
44. Ding X., Liu B., Yu Ph. A holistic lexicon-based approach to opinion mining // Proceedings of the Conference on Web Search and Web Data Mining (WSDM-2008). 2008. P. 231-240.
45. Neviarouskaya A., Prendinger H., Ishizuka M. Recognition of affect, judgment, and appreciation in text // Proceedings of the 23rd International Conference on Computational Linguistics (COLING-2010). 2010. P. 806-814.
46. Macdonald C., Santos R. L., Ounis I., Soboroff I. Blog track research at TREC // SIGIR Forum. 2010. V. 44, No 1. P. 58-75.
47. Dang H.T., Owczarzak K. Overview of the tac 2008 opinion question answering and summarization tasks // Proceedings of the First Text Analysis Conference. 2008.
48. Seki Y. et al. Overview of multilingual opinion analysis task at NTCIR-7 // Proceedings of the Seventh NTCIR Workshop. 2008. P. 185-203.
49. Pontiki M., Galanis D., Pavlopoulos J., Papageorgiou H., Androutsopoulos I., Manandhar S. SemEval-2014 Task 4: aspect based sentiment analysis // Proceedings of International Workshop on Semantic Evaluations SemEval-2014. 2014. P. 27-35.
50. Chetviorkin I., Loukachevitch N. Evaluating sentiment analysis systems in russian // Proceedings of BSNLP Workshop, ACL 2013. 2013. P. 12-16.
51. Loukachevitch N., Blinov P., Kotelnikov E., Rubtsova Y., Ivanov V., Tutubalina E. SentiRuEval: testing object-oriented sentiment analysis systems in Russian // Proceedings of International Conference of Computational Linguistics and Intellectual Technologies Dialog-2015. 2015. V. 2. P. 2-13.
52. Mikolov T., Sutskever I., Chen K., Corrado G.S., Dean J. Distributed representations of words and phrases and their compositionality // Advances in neural information processing systems. 2013. P. 3111-3119.
53. Blinov P.D., Kotelnikov E.V. Semantic similarity for aspect-based sentiment analysis // Proceedings of International Conference of Computational Linguistics and Intellectual Technologies Dialog-2015. 2015. V. 2. P. 23-33.
54. Mayorov V., Andrianov I., Astrakhantsev N., Avanesov V., Kozlov I., Turdakov D. A high precision method for aspect extraction in Russian // Proceedings of International Conference of Computational Linguistics and Intellectual Technologies Dialog-2015. V. 2. 2015. P. 34-43.
55. Tarasov D.S. Deep recurrent neural networks for multiple language aspect based sentiment analysis of user reviews // Proceedings of International Conference of Computational Linguistics and Intellectual Technologies Dialog-2015. 2015. V. 2. P. 53-64.
2. Amigo E., Corujo A., Gonzalo J., Meij E., Rijke M. Overview of RepLab 2012: Evaluating Online Reputation Management Systems // CLEF-2012. 2012.
3. Jiang Long, Mo Yu, Ming Zhou, Xiaohua Liu, Tiejun Zhao. Target dependent twitter sentiment classification // Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (ACL-2011). 2011. P. 151-160.
4. Popescu, A., Etzioni O. Extracting product features and opinions from reviews // Natural language processing and text mining. Springer: London. 2007. P. 9-28.
5. Liu B., Zhang L. A survey of opinion mining and sentiment analysis // Mining Text Data. Springer: US, 2012. P. 415-463.
6. Bagheri A., Saraee M., de Jong F. An unsupervised aspect detection model for sentiment analysis of reviews // Natural Language Processing and Information Systems. Springer: Berlin Heidelberg, 2013. P. 140-151.
7. Glavaš G., Korencic D., Šnajder J. Aspect-oriented opinion mining from user reviews in Croatian // Proceedings of BSNLP workshop, ACL-2013. 2013. P. 18-23.
8. Zhang L., Liu B. Aspect and entity extraction for opinion mining // Data Mining and Knowledge Discovery for Big Data. Springer: Berlin Heidelberg, 2014. P. 1-40.
9. Liu B. Sentiment analysis and Subjectivity // Handbook of Natural Language Processing. CRC Press, Taylor and Francis Group, Boca Raton, 2010. P. 1-38.
10. Gupta N.K. Extracting phrases describing problems with products and services from twitter messages // Computación y Sistemas. 2013. V. 17, No 2. P. 197-206.
11. Ivanov V., Tutubalina E. Clause-based approach to extracting problem phrases from user reviews of products // Analysis of Images, Social Networks and Texts. Springer International Publishing, 2014. P. 229-236.
12. Tutubalina E., Ivanov V. Unsupervised approach to extracting problem phrases from user reviews of products // Proceedings of the Aha! Workshop on Information Discovery in Texts, Coling-2014. 2014. P. 48-53.
13. Feng S., Bose R., Choi Y. Learning general connotation of words using graph-based algorithms // Proceedings of the Conference on Empirical Methods in Natural Language Processing. – Association for Computational Linguistics. 2011. P. 1092-1103.
14. Feng S., Kang J.S., Kuznetsova P., Choi Y. Connotation Lexicon: a dash of sentiment beneath the surface meaning // Proceedings of ACL. 2013. P. 1774-1784.
15. Zhang Lei, Bing Liu. Identifying noun product features that imply opinions // Proceedings of the Annual Meeting of the Association for Computational Linguistics (short paper) (ACL-2011). 2011. P. 575-580.
16. Liu B., Zhang L. A survey of opinion mining and sentiment analysis // Mining Text Data. 2012: Springer US. P. 415-463.
17. Zhang Lei, Liu B. Extracting resource terms for sentiment analysis // Proceedings of IJCNLP-2011. 2011. P.1171-1179.
18. Blair-Goldensohn S., Hannan K., McDonald R., Neylon T., Reis G. A., Reynar J. Building a sentiment summarizer for local service reviews // Proceedings of WWW Workshop on NLP in the Information Explosion Era. 2008.
19. Hu M., Liu B. Mining and summarizing customer reviews // Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2004. P. 168-177.
20. Марчук А.А., Уланов А.В., Макеев И.В., Чугреев А.А. Автоматическое извлечение параметров продуктов из текстов отзывов при помощи интернет-статистик // Труды Международной конференции «Компьютерная лингвистика и информационные технологии, Диалог-2013». 2013. Т. 2. C. 81-91.
21. Moghaddam S., Ester M. Aspect-based opinion mining from online reviews. Tutorial at SIGIR-2012. 2012.
22. Ku Lun-Wei, Yu-Ting Liang, Hsin-Hsi Chen. Opinion extraction, summarization and tracking in news and blog corpora // Proceedings of AAAI-CAAW'06. 2006.
23. Manning C.D., Raghavan P., Schütze H. Introduction to information retrieval. Cambridge: Cambridge University Press, 2008.
24. Scaffidi Ch., Bierhoff K., Chang E., Felker M., Ng H., Jin Ch. Red Opal: product-feature scoring from reviews // Proceedings of Twelfth ACM Conference on Electronic Commerce (EC-2007). 2007. P. 182-191.
25. Frantzi K., Ananiadou S., Mima H. Automatic recognition of multi-word terms: the C-value/NC-value method // International Journal on Digital Libraries, 2000. V. 3, No 2. P. 115-130.
26. Zhu J., Wang H., Tsou B., Zhu M. Multiaspect opinion polling from textual reviews // Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2009). 2009. P. 1799-1802.
27. Hai Z., Chang K., Cong G. One seed to find them all: mining opinion features via association // Proceedings of the 21st ACM international conference on Information and knowledge management. 2012. ACM. P. 255-264.
28. Qiu G., Liu B., Bu J., Chen C. Opinion word expansion and target extraction through double propagation // Computational Linguistics. 2011. V. 1, No 1. P. 1-18.
29. Loukachevitch N., Nokel M. An experimental study of term extraction for real information-retrieval thesauri // Proceedings of Terminology and Artificial Intelligence Conference TIA-2013. 2013. P. 69-78.
30. Zhuang L., Jing F., Zhu X. Movie review mining and summarization // Proceedings of ACM International Conference on Information and Knowledge Management (CIKM-2006), 2006. P. 43-50.
31. Zhang L., Liu B., Lim S., O’Brien-Strain E. Extracting and ranking product features in opinion documents // Proceedings of International Conference on Computational Linguistics (COLING-2010). 2010. P. 1462-1470.
32. Kovelamudi S., Ramalingam S., Sood A., Varma V. Domain independent model for product attribute extraction from user reviews using Wikipedia // Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP-2010). 2011. P. 1408-1412.
33. Niklas J., Gurevych I. Extracting opinion targets in a single and cross-domain setting with conditional random fields // Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2010). 2010. P. 1035-1045.
34. Choi Y., Cardie C. Hierarchical sequential learning for extracting opinions and their attributes // Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2010). 2010. P. 269-274.
35. Blei D., Ng A., Jordan M. Latent Dirichlet allocation // The Journal of Machine Learning Research, 2003. No 3. P. 993-1022.
36. Воронцов К.В., Потапенко А.А. Модификации EM-алгоритма для вероятностного тематического моделирования // Машинное обучение и анализ данных. 2013. Т. 1, № 6. C. 657-686.
37. Titov I., McDonald R. A joint model of text and aspect ratings for sentiment summarization // Urbana, 51, 61801. 2008.
38. Zhao Wayne Xin, Jing Jiang, Hongfei Yan, Xiaoming Li. Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid // Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2010). 2010. P. 56-65.
39. Zhai Z., Liu B., Xu H., Jia P. Grouping product features using semi-supervised learning with soft-constraints // Proceedings of Coling-2010. 2010. P. 1272-1280.
40. Zhai Z., Liu B., Xu H., Jia P. Clustering product features for opinion mining // Proceedings of the fourth ACM international conference on Web search and data mining. ACM. 2011. P. 347-354.
41. Yu J., Zha Z.J., Wang M., Wang K., Chua T.S. Domain-assisted product aspect hierarchy generation: towards hierarchical organization of unstructured consumer reviews // Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. 2011. P. 140-150.
42. Andrzejewski D., Zhu X., Craven M. Incorporating domain knowledge into topic modeling via Dirichlet forest priors // Proceedings of ICML. 2009. P. 25-32.
43. Mukherjee A., Liu B. Aspect extraction through semi-supervised modeling // Proceedings of 50th Annual Meeting of Association for Computational Linguistics (ACL-2012). 2012. P. 339-348.
44. Ding X., Liu B., Yu Ph. A holistic lexicon-based approach to opinion mining // Proceedings of the Conference on Web Search and Web Data Mining (WSDM-2008). 2008. P. 231-240.
45. Neviarouskaya A., Prendinger H., Ishizuka M. Recognition of affect, judgment, and appreciation in text // Proceedings of the 23rd International Conference on Computational Linguistics (COLING-2010). 2010. P. 806-814.
46. Macdonald C., Santos R. L., Ounis I., Soboroff I. Blog track research at TREC // SIGIR Forum. 2010. V. 44, No 1. P. 58-75.
47. Dang H.T., Owczarzak K. Overview of the tac 2008 opinion question answering and summarization tasks // Proceedings of the First Text Analysis Conference. 2008.
48. Seki Y. et al. Overview of multilingual opinion analysis task at NTCIR-7 // Proceedings of the Seventh NTCIR Workshop. 2008. P. 185-203.
49. Pontiki M., Galanis D., Pavlopoulos J., Papageorgiou H., Androutsopoulos I., Manandhar S. SemEval-2014 Task 4: aspect based sentiment analysis // Proceedings of International Workshop on Semantic Evaluations SemEval-2014. 2014. P. 27-35.
50. Chetviorkin I., Loukachevitch N. Evaluating sentiment analysis systems in russian // Proceedings of BSNLP Workshop, ACL 2013. 2013. P. 12-16.
51. Loukachevitch N., Blinov P., Kotelnikov E., Rubtsova Y., Ivanov V., Tutubalina E. SentiRuEval: testing object-oriented sentiment analysis systems in Russian // Proceedings of International Conference of Computational Linguistics and Intellectual Technologies Dialog-2015. 2015. V. 2. P. 2-13.
52. Mikolov T., Sutskever I., Chen K., Corrado G.S., Dean J. Distributed representations of words and phrases and their compositionality // Advances in neural information processing systems. 2013. P. 3111-3119.
53. Blinov P.D., Kotelnikov E.V. Semantic similarity for aspect-based sentiment analysis // Proceedings of International Conference of Computational Linguistics and Intellectual Technologies Dialog-2015. 2015. V. 2. P. 23-33.
54. Mayorov V., Andrianov I., Astrakhantsev N., Avanesov V., Kozlov I., Turdakov D. A high precision method for aspect extraction in Russian // Proceedings of International Conference of Computational Linguistics and Intellectual Technologies Dialog-2015. V. 2. 2015. P. 34-43.
55. Tarasov D.S. Deep recurrent neural networks for multiple language aspect based sentiment analysis of user reviews // Proceedings of International Conference of Computational Linguistics and Intellectual Technologies Dialog-2015. 2015. V. 2. P. 53-64.
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