How translation alters sentiment

Journal of Artificial Intelligence Research

Volumn 55, Issue , 2016, Pages 95-130

  Mohammad, Saif M ^a   Salameh, Mohammad ^b   Kiritchenko, Svetlana ^a  

^a NATIONAL RESEARCH COUNCIL CANADA   (Canada)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

SENTIMENT ANALYSIS;

ARABIC-ENGLISH; AUTOMATIC TRANSLATION; DIALECTAL ARABICS; MANUAL ANNOTATION; PARALLEL CORPORA; RESOURCE-RICH; SENTIMENT LEXICONS; STATE OF THE ART;

TRANSLATION (LANGUAGES);

EID: 84958157273     PISSN: 10769757     EISSN: None     Source Type: Journal    
DOI: 10.1613/jair.4787     Document Type: Article

References (60)

1. Abbasi, A., Chen, H., & Salem, A. (2008). Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Transactions on Information Systems, 26 (3), 12:1-12:34.
2. Abdul-Mageed, M., & Diab, M. (2014). SANA: A large scale multi-genre, multi-dialect lexicon for Arabic subjectivity and sentiment analysis. In Proceedings of the 9th In-ternational Conference on Language Resources and Evaluation, LREC '14. European Language Resources Association.
3. Abdul-Mageed, M., Diab, M., & Kübler, S. (2014). SAMAR: Subjectivity and sentiment analysis for Arabic social media. Computer Speech & Language, 28 (1), 20-37.
4. Abdul-Mageed, M., Diab, M. T., & Korayem, M. (2011). Subjectivity and sentiment analysis of modern standard Arabic. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 587-591.
5. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment analysis of Twitter data. In Proceedings of the Workshop on Languages in Social Media, LSM '11, pp. 30-38, Portland, Oregon.
6. Ahmad, K., Cheng, D., & Almas, Y. (2006). Multi-lingual sentiment analysis of financial news streams. In Proceedings of the 1st International Conference on Grid in Finance.
7. Ahmed, S., Pasquier, M., & Qadah, G. (2013). Key issues in conducting sentiment analysis on Arabic social media text. In Proceedings of the 9th International Conference on Innovations in Information Technology, pp. 72-77. IEEE.
8. Aisopos, F., Papadakis, G., Tserpes, K., & Varvarigou, T. (2012). Textual and contextual patterns for sentiment analysis over microblogs. In Proceedings of the 21st Inter-national Conference on World Wide Web Companion, WWW '12 Companion, pp. 453-454, New York, NY, USA.
9. Al-Kabi, M., Gigieh, A., Alsmadi, I., Wahsheh, H., & Haidar, M. (2013). An opinion analysis tool for colloquial and standard Arabic. In Proceedings of the 4th International Conference on Information and Communication Systems, ICICS '13.
10. Amini, M.-R., & Goutte, C. (2010). A co-classification approach to learning from multilingual corpora. Machine learning, 79 (1-2), 105-121.
11. Badaro, G., Baly, R., Hajj, H., Habash, N., & El-Hajj, W. (2014). A large scale Arabic sentiment lexicon for Arabic opinion mining. In Proceedings of the EMNLP Workshop on Arabic Natural Language Processing (ANLP), pp. 165-173, Doha, Qatar.
12. Bakliwal, A., Arora, P., Madhappan, S., Kapre, N., Singh, M., & Varma, V. (2012). Mining sentiments from tweets. In Proceedings of the 3rd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, WASSA '12, pp. 11-18, Jeju, Republic of Korea.
13. Balahur, A., & Turchi, M. (2014). Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Computer Speech & Language, 28 (1), 56-75.
14. Bellegarda, J. (2010). Emotion analysis using latent affective folding and embedding. In Proceedings of the NAACL-HLT Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 1-9, Los Angeles, California.
15. Boucouvalas, A. C. (2002). Real time text-to-emotion engine for expressive Internet communication. Emerging Communication: Studies on New Technologies and Practices in Communication, 5, 305-318.
16. Brody, S., & Diakopoulos, N. (2011). Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: using word lengthening to detect sentiment in microblogs. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '11, pp. 562-570, Stroudsburg, PA, USA.
17. Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2 (3), 27:1-27:27.
18. Chen, B., & Zhu, X. (2014). Bilingual sentiment consistency for statistical machine translation. In Proceedings of the 14th Conference of the European Chapter of the Associ-ation for Computational Linguistics, pp. 607-615, Gothenburg, Sweden. Association for Computational Linguistics.
19. Cherry, C., & Foster, G. (2012). Batch tuning strategies for statistical machine translation. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 427-436.
20. Chiang, D., Marton, Y., & Resnik, P. (2008). Online large-margin training of syntactic and structural translation features. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '08, pp. 224-233.
21. El-Beltagy, S. R., & Ali, A. (2013). Open issues in the sentiment analysis of Arabic social media: A case study. In Proceedings of the 9th International Conference on Innova-tions in Information Technology, pp. 215-220. IEEE.
22. El Kholy, A., & Habash, N. (2012). Orthographic and morphological processing for English|Arabic statistical machine translation. Machine Translation, 26 (1-2), 25-45.
23. Farra, N., Challita, E., Assi, R. A., & Hajj, H. (2010). Sentence-level and documentlevel sentiment mining for Arabic texts. In Proceedings of the IEEE International Conference on Data Mining Workshops, pp. 1114-1119. IEEE.
24. Genereux, M., & Evans, R. P. (2006). Distinguishing affective states in weblogs. In Pro-ceedings of the AAAI Spring Symposium on Computational Approaches to Analysing Weblogs, pp. 27-29, Stanford, California.
25. Gimpel, K., Schneider, N., O'Connor, B., Das, D., Mills, D., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., & Smith, N. A. (2011). Part-of-speech tagging for Twitter: Annotation, features, and experiments. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, ACL '11, pp. 42-47.
26. Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. Tech. rep., Stanford University.
27. Habash, N., Rambow, O., & Roth, R. (2009). MADA+TOKAN: A toolkit for Arabic tokenization, diacritization, morphological disambiguation, POS tagging, stemming and lemmatization. In Proceedings of the 2nd International Conference on Arabic Language Resources and Tools, pp. 102-109, Cairo, Egypt. The MEDAR Consortium.
28. Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '04, pp. 168-177, New York, NY, USA. ACM.
29. John, D., Boucouvalas, A. C., & Xu, Z. (2006). Representing emotional momentum within expressive Internet communication. In Proceedings of the 24th International Confer-ence on Internet and Multimedia Systems and Applications, pp. 183-188, Anaheim, CA. ACTA Press.
30. Kiritchenko, S., Mohammad, S., & Salameh, M. (2016). SemEval-2016 Task 7: Determining sentiment intensity of english and arabic phrases. In Proceedings of the International Workshop on Semantic Evaluation, SemEval '16.
31. Kiritchenko, S., Zhu, X., Cherry, C., & Mohammad, S. (2014a). NRC-Canada-2014: Detecting aspects and sentiment in customer reviews. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 437-442, Dublin, Ireland.
32. Kiritchenko, S., Zhu, X., & Mohammad, S. M. (2014b). Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research, 50, 723-762.
33. Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In Aggarwal, C. C., & Zhai, C. (Eds.), Mining Text Data, pp. 415-463. Springer US.
34. Liu, H., Lieberman, H., & Selker, T. (2003). A model of textual affect sensing using realworld knowledge. In Proceedings of the 8th International Conference on Intelligent User Interfaces, IUI '03, pp. 125-132, New York, NY. ACM.
35. Martínez-Cámara, E., Martín-Valdivia, M. T., Ureñalópez, L. A., & Montejoráez, A. R. (2012). Sentiment analysis in Twitter. Natural Language Engineering, 1-28.
36. Mihalcea, R., Banea, C., & Wiebe, J. (2007). Learning multilingual subjective language via cross-lingual projections. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, p. 976.
37. Mihalcea, R., & Liu, H. (2006). A corpus-based approach to finding happiness. In Pro-ceedings of the AAAI Spring Symposium on Computational Approaches to Analysing Weblogs, pp. 139-144. AAAI Press.
38. Mohammad, S. M. (2012). #Emotional tweets. In Proceedings of the First Joint Conference on Lexical and Computational Semantics, ∗SEM '12, pp. 246-255, Montréal, Canada.
39. Mohammad, S. M. (2016). Sentiment analysis: Detecting valence, emotions, and other affectual states from text. Emotion Measurement.
40. Mohammad, S. M., Kiritchenko, S., & Zhu, X. (2013). NRC-Canada: Building the stateof-the-art in sentiment analysis of tweets. In Proceedings of the 7th International Workshop on Semantic Evaluation Exercises, SemEval '13, Atlanta, Georgia, USA.
41. Mohammad, S. M., & Turney, P. D. (2010). Emotions evoked by common words and phrases: Using Mechanical Turk to create an emotion lexicon. In Proceedings of the NAACL-HLT Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 26-34, LA, California.
42. Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29 (3), 436-465.
43. Mohammad, S. M., & Yang, T. W. (2011). Tracking sentiment in mail: How genders differ on emotional axes. In Proceedings of the ACL Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, WASSA '11, pp. 70-79, Portland, OR, USA.
44. Mourad, A., & Darwish, K. (2013). Subjectivity and sentiment analysis of modern standard Arabic and Arabic microblogs. In Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA '13, pp. 55-64.
45. Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2011). Affect analysis model: novel rule-based approach to affect sensing from text. Natural Language Engineering, 17, 95-135.
46. Nie, J.-Y., Simard, M., Isabelle, P., & Durand, R. (1999). Cross-language information retrieval based on parallel texts and automatic mining of parallel texts from the Web. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 74-81. ACM.
47. Nielsen, F. Å. (2011). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. In Proceedings of the ESWC2011 Workshop on 'Making Sense of Micro-posts': Big things come in small packages, pp. 93-98.
48. Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the 7th Conference on International Language Resources and Evaluation, LREC '10, pp. 1320-1326, Valletta, Malta.
49. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2 (1-2), 1-135.
50. Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., & Manandhar, S. (2014). SemEval-2014 Task 4: Aspect based sentiment analysis. In Proceed-ings of the International Workshop on Semantic Evaluation, SemEval '14, pp. 27-35, Dublin, Ireland.
51. Refaee, E., & Rieser, V. (2014a). An Arabic Twitter corpus for subjectivity and sentiment analysis. In Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC '14, Reykjavik, Iceland. European Language Resources Association.
52. Refaee, E., & Rieser, V. (2014b). Subjectivity and sentiment analysis of Arabic Twitter feeds with limited resources. In Proceedings of the Workshop on Free/Open-Source Arabic Corpora and Corpora Processing Tools, p. 16.
53. Rosenthal, S., Ritter, A., Nakov, P., & Stoyanov, V. (2014). SemEval-2014 Task 9: Sentiment analysis in Twitter. In Proceedings of the 8th International Workshop on Semantic Evaluation, SemEval '14, pp. 73-80, Dublin, Ireland.
54. Salameh, M., Mohammad, S., & Kiritchenko, S. (2015). Sentiment after translation: A case-study on arabic social media posts. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 767-777, Denver, Colorado.
55. Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62 (2), 406-418.
56. Wilson, T., Kozareva, Z., Nakov, P., Rosenthal, S., Stoyanov, V., & Ritter, A. (2013). SemEval-2013 Task 2: Sentiment analysis in Twitter. In Proceedings of the Interna-tional Workshop on Semantic Evaluation, SemEval '13, Atlanta, Georgia, USA.
57. Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phraselevel sentiment analysis. In Proceedings of the Conference on Human Language Tech-nology and Empirical Methods in Natural Language Processing, HLT '05, pp. 347-354, Stroudsburg, PA, USA.
58. Zbib, R., Malchiodi, E., Devlin, J., Stallard, D., Matsoukas, S., Schwartz, R., Makhoul, J., Zaidan, O. F., & Callison-Burch, C. (2012). Machine translation of Arabic dialects. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 49-59. Association for Computational Linguistics.
59. Zhu, X., Guo, H., Mohammad, S., & Kiritchenko, S. (2014a). An empirical study on the effect of negation words on sentiment. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, ACL, Vol. 14.
60. Zhu, X., Kiritchenko, S., & Mohammad, S. (2014b). NRC-Canada-2014: Recent improvements in the sentiment analysis of tweets. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 443-447, Dublin, Ireland.