Rahim Dehkharghani
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Abstract
There is no doubt about the usefulness of public opinion toward different issues in social media and the World Wide Web. Extracting the feelings of people about an issue from text is not straightforward. Polarity lexicons that assign polarity tags or scores to words and phrases play an important role in sentiment analysis systems. As English is the richest language in this area, getting benefits from existing English resources in order to build new ones has attracted the interest of many researchers in recent years. In this article, we propose a new translation-based approach for building polarity resources in resource-lean languages such as Persian. The results of empirical evaluation of the proposed approach prove its effectiveness. The generated resource is the largest publicly available polarity lexicon for Persian.
- Gilbert Badaro, Ramy Baly, Hazem Hajj, Nizar Habash, and Wassim El-Hajj. 2014. A large scale Arabic sentiment lexicon for Arabic opinion mining. In Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP’14). 165--173.Google Scholar
Cross Ref
- Cristina Bosco, Viviana Patti, and Andrea Bolioli. 2013. Developing corpora for sentiment analysis: The case of irony and Senti-tut. IEEE Intelligent Systems 2 (2013), 55--63. Google ScholarDigital Library
- Erik Cambria, Daniel Olsher, and Dheeraj Rajagopal. 2014. SenticNet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis. In 28th AAAI Conference on Artificial Intelligence (AAAI'14). 1515--1521. Google ScholarDigital Library
- Erik Cambria, Bjorn Schuller, Yunqing Xia, and Catherine Havasi. 2013. New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems 28, 2 (2013), 15--21. Google ScholarDigital Library
- Simon Clematide and Manfred Klenner. 2010. Evaluation and extension of a polarity lexicon for German. In Proceedings of the 1st Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA'10). 7--13.Google Scholar
- Amitava Das and Sivaji Bandyopadhyay. 2010. SentiWordNet for Indian languages. In Proceedings of the 8th Workshop on Asian Language Resources (WALR'10). 56--63.Google Scholar
- Kia Dashtipour, Amir Hussain, Qiang Zhou, Alexander Gelbukh, Ahmad Y.A. Hawalah, and Erik Cambria. 2016. PerSent: A freely available Persian sentiment lexicon. In International Conference on Brain Inspired Cognitive Systems (BICS'16). Springer, 310--320.Google ScholarCross Ref
- Iman Dehdarbehbahani, Azadeh Shakery, and Heshaam Faili. 2014. Semi-supervised word polarity identification in resource-lean languages. Neural Networks 58 (2014), 50--59. Google ScholarDigital Library
- Rahim Dehkharghani, Yucel Saygin, Berrin Yanikoglu, and Kemal Oflazer. 2016. SentiTurkNet: A Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation 50, 3 (2016), 667--685. Google ScholarDigital Library
- Lingjia Deng and Janyce Wiebe. 2015. MPQA 3.0: An entity/event-level sentiment corpus. In The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT’15). 1323--1328. Retrieved from http://aclweb.org/anthology/N/N15/N15-1146.pdf.Google ScholarCross Ref
- Andrea Esuli and Fabrizio Sebastiani. 2006. SentiWordNet: A publicly available lexical resource for opinion mining. In Proceedings of International Conference on Language Resources and Evaluation (LREC'06), Vol. 6. 417--422.Google Scholar
- Joseph L. Fleiss. 1971. Measuring nominal scale agreement among many raters. Psychological Bulletin 76, 5 (1971), 378.Google ScholarCross Ref
- Catherine Havasi, Robert Speer, and Jason Alonso. 2007. ConceptNet 3: A flexible, multilingual semantic network for common sense knowledge. In Recent Advances in Natural Language Processing. 27--29.Google Scholar
- Geoffrey Holmes, Andrew Donkin, and Ian H. Witten. 1994. Weka: A machine learning workbench. In Proceedings of the 1994 2nd Australian and New Zealand Conference on Intelligent Information Systems (ANZIIS'94). IEEE, 357--361.Google Scholar
- David W. Hosmer Jr. and Stanley Lemeshow. 2004. Applied Logistic Regression. John Wiley 8 Sons.Google ScholarCross Ref
- Pedram Hosseini, Ali Ahmadian Ramaki, Hassan Maleki, Mansoureh Anvari, and Seyed Abolghasem Mirroshandel. 2018. SentiPers: A sentiment analysis corpus for Persian. Arxiv Preprint Arxiv:1801.07737 (2018).Google Scholar
- Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'04). ACM, 168--177. Google ScholarDigital Library
- Chihli Hung and Hao-Kai Lin. 2013. Using objective words in SentiWordNet to improve word-of-mouth sentiment classification. IEEE Intelligent Systems 28, 2 (2013), 47--54. Google ScholarDigital Library
- Bing Liu. 2012. Sentiment Analysis and Opinion Mining. Morgan and Claypool Publishers.Google ScholarDigital Library
- Xinfan Meng, Furu Wei, Ge Xu, Longkai Zhang, Xiaohua Liu, Ming Zhou, and Houfeng Wang. 2012. Lost in translations? Building sentiment lexicons using context based machine translation. In Proceeding of the 24th International Conference on Computational Linguistics (COLING'12). Indian Institute of Technology Bombay, 829--838.Google Scholar
- George A. Miller. 1995. WordNet: A lexical database for English. Communications of the ACM 38, 11 (1995), 39--41. Google ScholarDigital Library
- Saif M. Mohammad and Peter D. Turney. 2013. Crowdsourcing a word-emotion association lexicon. Computational Intelligence 29, 3 (2013), 436--465.Google Scholar
- Soujanya Poria, Alexander Gelbukh, Amir Hussain, Newton Howard, Dipankar Das, and Sivaji Bandyopadhyay. 2013. Enhanced SenticNet with affective labels for concept-based opinion mining. IEEE Intelligent Systems 28, 2 (2013), 31--38. Google ScholarDigital Library
- Verónica Pérez-rosas, Carmen Banea, and Rada Mihalcea. 2012. Learning sentiment lexicons in spanish. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12). 3077--3081.Google Scholar
- Behnam Sabeti, Pedram Hosseini, Gholamreza Ghassem-Sani, and Seyed Abolghasem Mirroshandel. 2016. LexiPers: An ontology based sentiment lexicon for Persian. In Proceedings of the 2nd Global Conference on Artificial Intelligence (GCAI'16). 329--339.Google Scholar
- Carlo Strapparava and Alessandro Valitutti. 2004. WordNet affect: An affective extension of Wordnet. In LREC, Vol. 4. 1083--1086.Google Scholar
- Angela Charng-Rurng Tsai, Chi-En Wu, Richard Tzong-Han Tsai, and Jane Yung-jen Hsu. 2013. Building a concept-level sentiment dictionary based on commonsense knowledge. IEEE Intelligent Systems 28, 2 (2013), 22--30. Google ScholarDigital Library
Index Terms
- SentiFars: A Persian Polarity Lexicon for Sentiment Analysis
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