Email this article Opinion Mining On Comments
Abstract
- The work opinion mining on comments is an web application, in which all the registered users were allowed to post their own ideas, leave comment to existing post. This application mainly focus on the comments by the registered users ,by mining all these comments individually through pattern matching concept , Here we are using Naive Bayes algorithm for pattern matching . The overall status of the post will be decided whether it is an positive or negative post by applying mining on comments and the status will be appeared along with the post by an emoji representation, if the number of positive comments are more than the number of negative comments then the post will be considered as a positive and an laughing emoji will be displayed along with the post otherwise it is considered as an negative post and an sad emoji will be appeared .such that the user can easily identify the status of the post easily without reading the overall content supported with the post.
References
Abbasi, Ahmed, Hsinchun Chen, and Arab Salem. Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Transactions on Information Systems (TOIS), 2008. 26(3).
Abdul-Mageed, Muhammad, Mona T. Diab, and Mohammed Korayem. Subjectivity and sentiment analysis of modern standard Arabic. in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:shortpapers. 2011.
Akkaya, Cem, Janyce Wiebe, and Rada Mihalcea. Subjectivity word sense disambiguation. in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP-2009). 2009.
Alm, Ebba Cecilia Ovesdotter. Affect in text and speech, 2008: ProQuest.
Andreevskaia, Alina and Sabine Bergler. Mining WordNet for fuzzy sentiment: Sentiment tag extraction from WordNet glosses. in Proceedings of Conference of the European Chapter of the Association for Computational Linguistics (EACL-06). 2006.
Andreevskaia, Alina and Sabine Bergler. When specialists and generalists work together: Overcoming domain dependence in sentiment tagging. in Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL-2008). 2008.
Andrzejewski, David and Xiaojin Zhu. Latent Dirichlet Allocation with topic-in-set knowledge. in Proceedings of NAACL HLT. 2009.
Andrzejewski, David, Xiaojin Zhu, and Mark Craven. Incorporating domain knowledge into topic modeling via Dirichlet forest priors. in Proceedings of ICML. 2009.
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