An Automated Real-Time System for Opinion Mining using a Hybrid Approach

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Author(s)

Indrajit Mukherjee 1,* Jasni M Zain 2 Prabhat Kumar Mahanti 3

1. Department of Computer Science and Engineering, BIT Mesra, Ranchi, INDIA

2. Faculty of Computer Systems & Software Engineering, University Malaysia Pahang, Malaysia

3. Department of CSAS, University of Brunswick, New Brunswick, Canada

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2016.07.06

Received: 5 Nov. 2015 / Revised: 11 Feb. 2016 / Accepted: 16 Apr. 2016 / Published: 8 Jul. 2016

Index Terms

Opinion Mining, Sentiment Analysis, Feature Extraction, Twitter Data Analysis, Graph based Sentiment Analysis, Data Extraction

Abstract

In this paper, a novel idea is being presented to perform Opinion Mining in a very simple and efficient manner with the help of the One-Level-Tree (OLT) based approach. To recognize opinions specific for features in customer reviews having a variety of features commingled with diverse emotions. Unlike some previous ventures entirely using one-time structured or filtered data but this is solely based on unstructured data obtained in real-time from Twitter. The hybrid approach utilizes the associations defined in Dependency Parsing Grammar and fully employs Double Propagation to extract new features and related new opinions within the review. The Dictionary based approach is used to expand the Opinion Lexicon. Within the dependency parsing relations a new relation is being proposed to more effectively catch the associations between opinions and features. The three new methods are being proposed, termed as Double Positive Double Negative (DPDN), Catch-Phrase Method (CPM) & Negation Check (NC), for performing criteria specific evaluations. The OLT approach conveniently displays the relationship between the features and their opinions in an elementary fashion in the form of a graph. The proposed system achieves splendid accuracy across all domains and also performs better than the state-of-the-art systems.

Cite This Paper

Indrajit Mukherjee, Jasni M Zain, P. K. Mahanti, "An Automated Real-Time System for Opinion Mining using a Hybrid Approach", International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.7, pp.55-64, 2016. DOI:10.5815/ijisa.2016.07.06

Reference

[1]Hatzivassiloglou, Vasileios and Kathleen R. McKeown, “Predicting the semantic orientation of adjectives”, In Proceedings of ACL’97, pages 174–181. Stroudsburg, PA, 1997.
[2]Wiebe, Janyce, “Learning subjective adjective from corpora.”, In Proceedings of AAAI’00, pages 735–740, 2000.
[3]Wiebe, Janyce, Theresa Wilson, Rebecca Bruce, Matthew Bell, and Melanie Martin, “Learning subjective language”, Computational Linguistics, 30(3):277–308, 2004.
[4]Turney, Peter D. and Michael L. Littman, “Measuring praise and criticism: Inference of semantic orientation from association”, ACM Transactions on Information System, 21(4):315–346, 2003.
[5]Kamps, Jaap, Maarten Marx, Robert J. Mokken, and Maarten de Rijke, “Using Wordnet to measure semantic orientation of adjectives”, In Proceedings of LREC’04, pages 1115–1118, 2004.
[6]Takamura, Hiroya, Takashi Inui, and Manabu Okumura, “Extracting semantic orientations of words using spin model”, In Proceedings of ACL’05,pages 133–140, 2005.
[7]Hu, Mingqing and Bing Liu, “Mining and summarizing customer reviews”, In Proceedings of SIGKDD’04, pages 168–177, 2004.
[8]Kim, Soo-Min and Eduard Hovy, “Determining the sentiment of opinions”, In Proceedings of COLING’04,pages 1367–1373, 2004.
[9]Himabindu Lakkaraju, Chiranjib Bhattacharyya, Indrajit Bhattacharya and Srujanaerugu,. “Exploiting Coherence for the simultaneous discovery of latent facets and associated sentiments”, SIAM International Conference on Data Mining (SDM),2011.
[10]Minqing Hu and Bing Liu, “Mining and summarizing customer reviews”, KDD '04:Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004.
[11]Chen Mosha, “Combining Dependency Parsing with Shallow Semantic Analysis for Chinese Opinion-Element Relation Identification”, IEEE, pp.299-305, 2010.
[12]Yuanbin Wu, Qi Zhang, Xuanjing Huang, Lide Wu, “Phrase Dependency Parsing for Opinion Mining”, EMNLP '09 Proceedings of the 2009 Conference on Empirical Methodsin Natural Language Processing, Volume 3, 2009.
[13]Qi Zhang, Yuanbin Wu, Tao Li, Mitsunori Ogihara, Joseph Johnson, Xuanjing Huang, “Mining Product Reviews Based on Shallow Dependency Parsing”, SIGIR09, Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009.
[14]M. Mathioudakis and N. Koudas, “Twitter monitor: Trend Detection over the Twitter Stream”, In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pages 1155–1158. ACM, 2010.
[15]F. Morstatter, S. Kumar, H. Liu, and R. Maciejewski, “Understanding Twitter Data with Tweet Xplorer”, In Proceedings of the 2013 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM,2013.
[16]Hong Yu and Vasileios Hatzivassiloglou, “Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences”, In Proceedings of 8th Conference on Empirical Methods in Natural Language Processing(EMNLP’03), Sapporo, Japan, 2003.
[17]Theresa Wilson, Janyce Wiebe, and Paul Hoffmann, “Recognizing contextual polarity in phrase level sentiment analysis”, In Proceedings of Human Language Technology Conference and Empirical Methods in Natural Language Processing Conference(HLT/EMNLP’05), Vancouver, Canada, 2005.
[18]Peter D. Turney, “Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews”, In Proceedings of 40th Annual Meeting of the Association for Computational Linguistics (ACL’02), Philadelphia, USA, 2002.
[19]Chang, Chih-Chung, and Chih-Jen Lin, "LIBSVM: a library for support vector machines", Transactions on Intelligent Systems and Technology (TIST) 2.3: 27, ACM, 2011.
[20]O'Connor, Brendan, et al., "From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series", ICWSM 11: 122-129, 2010.
[21]Chang, Chih-Chung, and Chih-Jen Lin, "LIBSVM: a library for support vector machines." ACM Transactions on Intelligent Systems and Technology (TIST) 2.3: 27, 2011.
[22]Cheng, Zhiyuan, James Caverlee, and Kyumin Lee, "You are where you tweet: a content-based approach to geo-locating twitter users", Proceedings of the 19th ACM international conference on Information and knowledge management, ACM, 2010.
[23]Agarwal, Apoorv, et al., "Sentiment analysis of twitter data", Proceedings of the Workshop on Languages in Social Media, Association for Computational Linguistics, 2011.
[24]Pak, Alexander, and Patrick Paroubek, "Twitter as a Corpus for Sentiment Analysis and Opinion Mining." LREC, 2010.
[25]Greg Gorbach, “In Dynamic Market, Consumer Goods Companies Rely on Manufacturing Operations Management Systems”, ARC view, 2010.
[26]Erik Cambria, Björn Schuller, Yunqing Xia and Catherine Havasi, Knowledge-Based Approaches to Concept-Level Sentiment Analysis, IEEE Transaction on Intelligent System, 2013.
[27]Go, Alec, Richa Bhayani, and Lei Huang. "Twitter sentiment classification using distant supervision." CS224N Project Report, Stanford (2009): 1-12.
[28]Shamanth Kumar, Fred Morstatter, Huan Liu, “Twitter Data Analytics”, Springer, 2013.