Ranking products with IF-based sentiment word framework and TODIM method

The purpose of this paper is to rank products by combining sentiment analysis (SA) and multiple attribute decision-making (MADM).,This research constructs intuitionistic fuzzy (IF)-based sentiment word framework and corresponding computation rules, which aim to calculate the sentiment score of each sentiment phase. Based on intuitionistic fuzzy weighted averaging operator, the authors aggregate the overall performance of each feature for different products. Then, the MADM method can be used, TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) in this paper, to rank product through online reviews.,The results of the research show the superiority and applicability of proposed method in ranking products with online reviews.,This paper proposes IF-based sentiment word framework and corresponding computation rules, which is a novel idea to express both the sentiment orientations (positive, negative and neutral) and emotional intensities. In addition, this research makes full use of knowledge from both experts and online reviewers. Further, attention degree of each feature is considered in the process of calculating weight of different features, which is rarely seen in current studies. This paper makes full use of SA, fuzzy control theory and MADM theory to handle vague information (online comments) and rank alternative products, which can promote future perspectives and developments.

[1]  Rohini S. Rahate,et al.  Feature Selection for Sentiment Analysis by using SVM , 2013 .

[2]  Taro Watanabe,et al.  Optimization for Statistical Machine Translation: A Survey , 2016, CL.

[3]  Zeshui Xu,et al.  Intuitionistic Fuzzy Aggregation Operators , 2007, IEEE Transactions on Fuzzy Systems.

[4]  Flavius Frasincar,et al.  Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data , 2018, IEEE Transactions on Cybernetics.

[5]  Meng Wang,et al.  Product Aspect Ranking and Its Applications , 2014, IEEE Transactions on Knowledge and Data Engineering.

[6]  Zeshui Xu,et al.  Some similarity measures of intuitionistic fuzzy sets and their applications to multiple attribute decision making , 2007, Fuzzy Optim. Decis. Mak..

[7]  Dwayne D. Gremler,et al.  Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? , 2004 .

[8]  Rudy Prabowo,et al.  Sentiment analysis: A combined approach , 2009, J. Informetrics.

[9]  Usman Qamar,et al.  Multi-Objective Model Selection (MOMS)-based Semi-Supervised Framework for Sentiment Analysis , 2016, Cognitive Computation.

[10]  Stephen Shaoyi Liao,et al.  Mining comparative opinions from customer reviews for Competitive Intelligence , 2011, Decis. Support Syst..

[11]  Sanyang Liu,et al.  A New Score Function for Fuzzy MCDM Based on Vague Set Theory , 2006 .

[12]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[13]  Martin Haselmayer,et al.  Sentiment analysis of political communication: combining a dictionary approach with crowdcoding , 2016, Quality & Quantity.

[14]  Kang Liu,et al.  Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu , 2015, CL.

[15]  Habib Ullah Khan,et al.  CAPRA: a comprehensive approach to product ranking using customer reviews , 2015, Computing.

[16]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

[17]  Adnan Acan,et al.  A multiagent, dynamic rank-driven multi-deme architecture for real-valued multiobjective optimization , 2016, Artificial Intelligence Review.

[18]  Juan Luis Castro,et al.  Lexicon-based Comments-oriented News Sentiment Analyzer system , 2012, Expert Syst. Appl..

[19]  A. Choudhary,et al.  Mining millions of reviews: a technique to rank products based on importance of reviews , 2011, ICEC '11.

[20]  G. S. Mahalakshmi,et al.  Twitter Sentiment Analysis for Large-Scale Data: An Unsupervised Approach , 2014, Cognitive Computation.

[21]  Lei Xie,et al.  Topic modeling in multimedia: algorithms and applications , 2015, Soft Comput..

[22]  Usman Qamar,et al.  Lexicon based semantic detection of sentiments using expected likelihood estimate smoothed odds ratio , 2016, Artificial Intelligence Review.

[23]  Yi Peng,et al.  A Fuzzy PROMETHEE Approach for Mining Customer Reviews in Chinese , 2014, Arabian Journal for Science and Engineering.

[24]  Walaa Medhat,et al.  Sentiment analysis algorithms and applications: A survey , 2014 .

[25]  Zhaoyu Li,et al.  Naive Bayes algorithm for Twitter sentiment analysis and its implementation in MapReduce , 2014 .

[26]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[27]  Usman Qamar,et al.  eSAP: A decision support framework for enhanced sentiment analysis and polarity classification , 2016, Inf. Sci..

[28]  Nan Jiang,et al.  An improved algorithm for sentiment analysis based on maximum entropy , 2017, Soft Computing.

[29]  Shinsuke Tanabe,et al.  Persistent organochlorine residues in air, water, sediments, and soils from the lake baikal region, Russia. , 1995, Environmental science & technology.

[30]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[31]  Li Chen,et al.  News impact on stock price return via sentiment analysis , 2014, Knowl. Based Syst..

[32]  Yang Liu,et al.  Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory , 2017, Inf. Fusion.