An Algorithmic Approach based on Principal Component Analysis for Aspect-based Opinion Summarization

Summarization helps in reducing the text in the shortest way possible such that significant properties in the text remain preserved and important information can be gained from the text. A novel approach is developed and summaries are generated using extractive based technique using Principal Component Analysis. The advantages of the proposed method lie in greater computational efficiency, data understanding, robustness and handling sparse data. This paper discusses the aspect based opinion summarization problem by proposing a novel unsupervised method by combining theories of rational awareness with sentence dependency trees to identify aspects. The results are carried out on dissimilar datasets consisting of numerous opinions and comparison with the previous based approaches demonstrates the success of the work. The results on Opinosis dataset are reported by measuring using ROUGE tool. The three random individuals are contacted for reference summaries which are compared with the system generated gold summaries for conducting subjective evaluation.

[1]  Renata Vieira,et al.  Ontology based feature level opinion mining for portuguese reviews , 2013, WWW '13 Companion.

[2]  Saadat M. Alhashmi,et al.  Performance and trends in recent opinion retrieval techniques , 2013, The Knowledge Engineering Review.

[3]  Sasha Blair-Goldensohn,et al.  Building a Sentiment Summarizer for Local Service Reviews , 2008 .

[4]  Bing Liu,et al.  Mining Opinion Features in Customer Reviews , 2004, AAAI.

[5]  Ramakrishnan Srikant,et al.  Mining newsgroups using networks arising from social behavior , 2003, WWW '03.

[6]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[7]  Durga Toshniwal,et al.  Feature based Summarization of Customers' Reviews of Online Products , 2013, KES.

[8]  Dragomir R. Radev,et al.  Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies , 2000, ArXiv.

[9]  Latesh G. Malik,et al.  ATSSC: Development of an approach based on soft computing for text summarization , 2017, Comput. Speech Lang..

[10]  Victor Zue,et al.  Harvesting and Summarizing User-Generated Content for Advanced Speech-Based HCI , 2012, IEEE Journal of Selected Topics in Signal Processing.

[11]  Sang-goo Lee,et al.  Opinion mining of customer feedback data on the web , 2008, ICUIMC '08.

[12]  L. Ghaoui,et al.  Sparse PCA for Text Corpus Summarization and Exploration , 2010 .

[13]  George A. Miller,et al.  Introduction to WordNet: An On-line Lexical Database , 1990 .

[14]  Nilanjan Dey,et al.  Internet of Things and Big Data Analytics Toward Next-Generation Intelligence , 2018 .

[15]  Oren Etzioni,et al.  OPINE: Extracting Product Features and Opinions from Reviews , 2005, HLT/EMNLP.

[16]  Jun Zhao,et al.  Opinion Target Extraction Using Partially-Supervised Word Alignment Model , 2013, IJCAI.

[17]  Yuefeng Li,et al.  Aspect-Based Opinion Extraction from Customer reviews , 2014, CSE 2014.

[18]  Hugo Liu,et al.  ConceptNet — A Practical Commonsense Reasoning Tool-Kit , 2004 .

[19]  Jiawei Han,et al.  Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions , 2010, COLING.

[20]  Vijay B. Raut.  Survey on Opinion Mining and Summarization of User Reviews on Web , 2014 .

[21]  Yamina Tlili Guiyassa,et al.  Mining explicit and implicit opinions from reviews , 2016, Int. J. Data Min. Model. Manag..

[22]  Manisha Sharma,et al.  A Novel Approach for Crawling the Opinions from World Wide Web , 2016, Int. J. Inf. Retr. Res..

[23]  Zili Zhang,et al.  Sentiment classification of Internet restaurant reviews written in Cantonese , 2011, Expert Syst. Appl..

[24]  Subhabrata Mukherjee,et al.  Sentiment Aggregation using ConceptNet Ontology , 2013, IJCNLP.

[25]  Rupal Bhargava,et al.  ATSSI: Abstractive Text Summarization Using Sentiment Infusion , 2016 .

[26]  Komal Kumar Bhatia,et al.  Opinion Score Mining: An Algorithmic Approach , 2017 .

[27]  Shou-de Lin,et al.  Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP) , 2014 .

[28]  Zhiyuan Liu,et al.  Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data , 2018, Lecture Notes in Computer Science.

[29]  Chin-Yew Lin,et al.  ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.

[30]  ChengXiang Zhai,et al.  Comprehensive Review of Opinion Summarization , 2011 .

[31]  Chun Chen,et al.  Opinion Word Expansion and Target Extraction through Double Propagation , 2011, CL.

[32]  Chien-Liang Liu,et al.  Movie Rating and Review Summarization in Mobile Environment , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[33]  Stephan Kreutzer,et al.  Graph operations on parity games and polynomial-time algorithms , 2016, Theor. Comput. Sci..

[34]  Walter Daelemans,et al.  Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2014, EMNLP 2014.

[35]  Yu-N Cheah,et al.  Aspect extraction in sentiment analysis: comparative analysis and survey , 2016, Artificial Intelligence Review.

[36]  J. van Leeuwen,et al.  Intelligent Data Engineering and Automated Learning , 2003, Lecture Notes in Computer Science.

[37]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.