Sentiment Analysis on Online Product Reviews

Today, people are exchanging their thoughts through online Web forums, blogs, and different social media platforms. Sometimes, they are giving reviews and opinions on different products, brand, and their services. Their reviews toward a product not only improve the product quality but also influence purchase decisions of the consumers. Thus, product review analysis is a widely accepted platform where consumer can easily aware of their requirements. In this experiment, we track 568,454 fine food reviews of 74,258 products and 256,059 users on Amazon over a period of ten years. To analyze the result, we select six most popular products and users based on the plain text review, and NRC emotion lexicon is used which can be categorized eight basic emotions and two sentiments. Word cloud also help our research to make comparisons between the eight emotion categories. Our results show that how sentiment analysis will help to identify the consumers’ behaviors and overcome those risks to meet the consumers’ satisfaction.

[1]  Saleem Abuleil,et al.  Using NLP Approach for Analyzing Customer Reviews , 2017 .

[2]  K. Vithiya Ruba,et al.  Building a Custom Sentiment Analysis Tool based on an Ontology for Twitter Posts , 2015 .

[3]  João Gama,et al.  MARKETING RESEARCH: THE ROLE OF SENTIMENT ANALYSIS , 2013 .

[4]  Peter D. Turney,et al.  Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon , 2010, HLT-NAACL 2010.

[5]  Amlan Chakrabarti,et al.  Twitter sentiment analysis for product review using lexicon method , 2017, 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI).

[6]  Saif Mohammad,et al.  CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON , 2013, Comput. Intell..

[7]  Sandip Roy,et al.  Analyzing Political Sentiment Using Twitter Data , 2018, Information and Communication Technology for Intelligent Systems.

[8]  Andrea Esuli,et al.  SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining , 2006, LREC.

[9]  Julian J. McAuley,et al.  Addressing Complex and Subjective Product-Related Queries with Customer Reviews , 2015, WWW.

[10]  Vibhu O. Mittal,et al.  Comparative Experiments on Sentiment Classification for Online Product Reviews , 2006, AAAI.

[11]  Justin Zhijun Zhan,et al.  Sentiment analysis using product review data , 2015, Journal of Big Data.

[12]  M. Geetha,et al.  Relationship between customer sentiment and online customer ratings for hotels - An empirical analysis , 2017 .

[13]  Jyothi Shetty,et al.  Sentiment analysis of product reviews: A review , 2017, 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT).

[14]  U Ravi Babu Sentiment Analysis of Reviews for E-Shopping Websites , 2017 .

[15]  Masrah Azrifah Azmi Murad,et al.  The effects of pre-processing strategies in sentiment analysis of online movie reviews , 2017 .

[16]  Cane Leung,et al.  Sentiment Analysis of Product Reviews , 2009, Encyclopedia of Data Warehousing and Mining.

[17]  Siu Cheung Hui,et al.  Word Cloud Model for Text Categorization , 2011, 2011 IEEE 11th International Conference on Data Mining.

[18]  Wei Wang,et al.  Ranking product aspects through sentiment analysis of online reviews , 2017, J. Exp. Theor. Artif. Intell..

[19]  Raphaël Troncy,et al.  Sentiment Polarity Detection from Amazon Reviews: An Experimental Study , 2016, SemWebEval@ESWC.

[20]  Quratulain Rajput,et al.  Lexicon-Based Sentiment Analysis of Teachers' Evaluation , 2016, Appl. Comput. Intell. Soft Comput..

[21]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[22]  Lin Li,et al.  Identification of informative reviews enhanced by dependency parsing and sentiment analysis , 2016, 2016 First IEEE International Conference on Computer Communication and the Internet (ICCCI).

[23]  Kumaravelan Gopalakrishnan,et al.  Intensified Sentiment Analysis of Customer Product Reviews Using Acoustic and Textual Features , 2016 .

[24]  Maria Virvou,et al.  The effect of preprocessing techniques on Twitter sentiment analysis , 2016, 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA).

[25]  Jure Leskovec,et al.  From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews , 2013, WWW.

[26]  Xiaonian He,et al.  The Crossing Number of Cartesian Products of Stars with 5-Vertex Graphs , 2010, CI 2010.