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.

Keyword

Sentiment analysis, Opinion summarization, K-means clustering, Genetic algorithm, Sentiment analysis, Word embedding.

Cite this article

Refference

[1][1]Bhatia N, Jaiswal A. Trends in extractive and abstractive techniques in text summarization. International Journal of Computer Applications. 2015; 117(6):21-4.

[2][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.

[3][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.

[4][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.

[5][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.

[6][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.

[7][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.

[8][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.

[9][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.

[10][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.

[11][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.

[12][12]Wang D, Liu Y. Opinion summarization on spontaneous conversations. Computer Speech & Language. 2015; 34(1):61-82.

[13][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.

[14][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.

[15][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.

[16][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).

[17][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.

[18][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.

[19][19]Huang Y, Shen C, Li T. Event summarization for sports games using twitter streams. World Wide Web. 2018; 21(3):609-27.

[20][20]Abdi A, Shamsuddin SM, Aliguliyev RM. QMOS: query-based multi-documents opinion-oriented summarization. Information Processing & Management. 2018; 54(2):318-38.

[21][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.

[22][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.

[23][23]Zhang C, Zhou Q. Online investigation of users’ attitudes using automatic question answering. Online Information Review. 2018; 42(3):419-35.

[24][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.

[25][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.

[26][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.

[27][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.