(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-6 Issue-52 March-2019
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Paper Title : A survey on sentimental cluster based opinion summarization in question answering community
Author Name : Ankur Jivapuri Goswami
Abstract :

A sentiment analysis is a study which includes opinion mining, sentiment classification, and opinion summarization broadly. An opinion summarization plays an increasing research interest for automatically compressing the extensive information and generating a short summary with unlimited time. Opinion analysis is one of the emerging studies in computer domain which embrace of sentiment polarity, sentiment, opinion or semantic orientation. This paper presents the survey on sentiment analysis and summarization approaches with its challenges, methodology and pros and cons of the existing methodology. In this survey, we evaluated the research gaps of the existing technique for suggesting the new technique by the mean of applying the semi-supervised data undergo clustering; classification and summarization by means of convolutional neural network (CNN) network learning method which may use for the opinion summarization.

Keywords : Sentiment analysis, Opinion summarization, K-means clustering, Genetic algorithm, Sentiment analysis, Word embedding.
Cite this article : Goswami AJ. A survey on sentimental cluster based opinion summarization in question answering community. International Journal of Advanced Technology and Engineering Exploration. 2019; 6(52):71-76. DOI:10.19101/IJATEE.2019.650016.
References :
[1]Bhatia N, Jaiswal A. Trends in extractive and abstractive techniques in text summarization. International Journal of Computer Applications. 2015; 117(6):21-4.
[Google Scholar]
[2]Moratanch N, Chitrakala S. A survey on abstractive text summarization. In international conference on circuit, power and computing technologies 2016 (pp. 1-7). IEEE.
[Crossref] [Google Scholar]
[3]Liu F, Flanigan J, Thomson S, Sadeh N, Smith NA. Toward abstractive summarization using semantic representations. Annual conference of the North American chapter of the ACL 2018 (pp. 1077–86). Association for Computational Linguistics.
[Google Scholar]
[4]Wang L, Raghavan H, Castelli V, Florian R, Cardie C. A sentence compression based framework to query-focused multi-document summarization. Proceedings of the annual meeting of the ACL 2016 (pp. 1384–94). Association for Computational Linguistics.
[Google Scholar]
[5]Wang L, Raghavan H, Cardie C, Castelli V. Query-focused opinion summarization for user-generated content. Proceedings of the international conference on computational linguistics 2014 (pp. 1660–9). Association for Computational Linguistics.
[Google Scholar]
[6]Lloret E, Boldrini E, Vodolazova T, Martínez-Barco P, Muñoz R, Palomar M. A novel concept-level approach for ultra-concise opinion summarization. Expert Systems with Applications. 2015; 42(20):7148-56.
[Crossref] [Google Scholar]
[7]Ali F, Kim EK, Kim YG. Type-2 fuzzy ontology-based opinion mining and information extraction: a proposal to automate the hotel reservation system. Applied Intelligence. 2015; 42(3):481-500.
[Google Scholar]
[8]Ali F, Kwak KS, Kim YG. Opinion mining based on fuzzy domain ontology and support vector machine: a proposal to automate online review classification. Applied Soft Computing. 2016; 47:235-50.
[Crossref] [Google Scholar]
[9]Wu H, Gu Y, Sun S, Gu X. Aspect-based opinion summarization with convolutional neural networks. In international joint conference on neural networks 2016 (pp. 3157-63). IEEE.
[Crossref] [Google Scholar]
[10]Fang Q, Xu C, Sang J, Hossain MS, Muhammad G. Word-of-mouth understanding: entity-centric multimodal aspect-opinion mining in social media. IEEE Transactions on Multimedia. 2015; 17(12):2281-96.
[Crossref] [Google Scholar]
[11]Somprasertsri G, Lalitrojwong P. Mining feature-opinion in online customer reviews for opinion summarization. Journal of Universal Computer Science. 2010; 16(6):938-55.
[Google Scholar]
[12]Wang D, Liu Y. Opinion summarization on spontaneous conversations. Computer Speech & Language. 2015; 34(1):61-82.
[Crossref] [Google Scholar]
[13]Yang G, Wen D, Chen NS, Sutinen E. A novel contextual topic model for multi-document summarization. Expert Systems with Applications. 2015; 42(3):1340-52.
[Crossref] [Google Scholar]
[14]Liu CY, Chen MS, Tseng CY. IncreSTS: towards real-time incremental short text summarization on comment streams from social network services. IEEE Transactions on Knowledge and Data Engineering. 2015; 27(11):2986-3000.
[Crossref] [Google Scholar]
[15]Zhou X, Wan X, Xiao J. CMiner: opinion extraction and summarization for Chinese microblogs. IEEE Transactions on Knowledge and Data Engineering. 2016; 28(7):1650-63.
[Crossref] [Google Scholar]
[16]Jha V, Ramu S, Shenoy PD, Venugopal KR. Reputation systems: evaluating reputation among all good sellers. Data-Enabled Discovery and Applications. 2017; 1(8).
[Crossref] [Google Scholar]
[17]Liu M, Fang Y, Choulos AG, Park DH, Hu X. Product review summarization through question retrieval and diversification. Information Retrieval Journal. 2017; 20(6):575-605.
[Crossref] [Google Scholar]
[18]AL-Sharuee MT, Liu F, Pratama M. Sentiment analysis: an automatic contextual analysis and ensemble clustering approach and comparison. Data & Knowledge Engineering. 2018; 115:194-213.
[Crossref] [Google Scholar]
[19]Huang Y, Shen C, Li T. Event summarization for sports games using twitter streams. World Wide Web. 2018; 21(3):609-27.
[Crossref] [Google Scholar]
[20]Abdi A, Shamsuddin SM, Aliguliyev RM. QMOS: query-based multi-documents opinion-oriented summarization. Information Processing & Management. 2018; 54(2):318-38.
[Crossref] [Google Scholar]
[21]Kang M, Ahn J, Lee K. Opinion mining using ensemble text hidden Markov models for text classification. Expert Systems with Applications. 2018; 94:218-27.
[Crossref] [Google Scholar]
[22]Rudra K, Sharma A, Ganguly N, Imran M. Classifying and summarizing information from microblogs during epidemics. Information Systems Frontiers. 2018; 20(5):933-48.
[Crossref] [Google Scholar]
[23]Zhang C, Zhou Q. Online investigation of users’ attitudes using automatic question answering. Online Information Review. 2018; 42(3):419-35.
[Crossref] [Google Scholar]
[24]Wu P, Zhou Q, Lei Z, Qiu W, Li X. Template oriented text summarization via knowledge graph. In international conference on audio, language and image processing (ICALIP) 2018 (pp. 79-83). IEEE.
[Crossref] [Google Scholar]
[25]Abdi A, Shamsuddin SM, Hasan S, Piran J. Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment. Expert Systems with Applications. 2018; 109:66-85.
[Crossref] [Google Scholar]
[26]Ali SM, Noorian Z, Bagheri E, Ding C, Al-Obeidat F. Topic and sentiment aware microblog summarization for twitter. Journal of Intelligent Information Systems. 2018:1-28.
[Crossref] [Google Scholar]
[27]Rautray R, Balabantaray RC. An evolutionary framework for multi document summarization using Cuckoo search approach: MDSCSA. Applied Computing and Informatics. 2018; 14(2):134-44.
[Crossref] [Google Scholar]