Customer review summarization approach using Twitter and SentiWordNet

Since E-commerce is becoming more and more popular, the number of customer reviews raises rapidly. Opinions on the Web affect our choices and decisions. Thus, it becomes necessary to automatically process a mixture of reviews and prepare to the customer the required information in an appropriate form. In the same context, we present a new approach of feature-based opinion summarization which aims to turn the customer reviews into scores that measure the customer satisfaction for a given product and its features. These scores are between 0 and 1 and can be used for decision making and then help users in their choices. We investigated opinions extracted from nouns, adjectives, verbs and adverbs contrary to previous researches which use essentially adjectives. Experimental results show that our method performs comparably to classic feature-based summarization methods.

[1]  John Blitzer,et al.  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.

[2]  Sabine Bergler,et al.  Semantic Tag Extraction from WordNet Glosses , 2006, LREC.

[3]  Olfa Nasraoui,et al.  Web data mining: exploring hyperlinks, contents, and usage data , 2008, SKDD.

[4]  Sabine Bergler,et al.  Mining WordNet for a Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses , 2006, EACL.

[5]  Jonathon Read,et al.  Using Emoticons to Reduce Dependency in Machine Learning Techniques for Sentiment Classification , 2005, ACL.

[6]  Takashi Inui,et al.  Extracting Semantic Orientations of Words using Spin Model , 2005, ACL.

[7]  Andrea Esuli,et al.  Determining the semantic orientation of terms through gloss classification , 2005, CIKM '05.

[8]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[9]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

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

[11]  Nizar Y. Habash,et al.  Handbook of Natural Language Processing, Second Edition , 2010 .

[12]  Ellen Riloff,et al.  Learning subjective nouns using extraction pattern bootstrapping , 2003, CoNLL.

[13]  M. de Rijke,et al.  UvA-DARE ( Digital Academic Repository ) Using WordNet to measure semantic orientations of adjectives , 2004 .

[14]  Zellig S. Harris,et al.  Mathematical structures of language , 1968, Interscience tracts in pure and applied mathematics.

[15]  Soo-Min Kim,et al.  Determining the Sentiment of Opinions , 2004, COLING.

[16]  Rim Faiz,et al.  Automatic extraction and classification approach of opinions in texts , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[17]  Hiroshi Kanayama,et al.  Fully Automatic Lexicon Expansion for Domain-oriented Sentiment Analysis , 2006, EMNLP.

[18]  Rim Faiz,et al.  Hybrid Method for Computing Word-Pair Similarity based on Web Content , 2012, WIMS '12.

[19]  Patrick Paroubek,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2010, LREC.

[20]  Clement T. Yu,et al.  Construction of a sentimental word dictionary , 2010, CIKM '10.

[21]  Patrick Paroubek,et al.  Twitter Based System: Using Twitter for Disambiguating Sentiment Ambiguous Adjectives , 2010, *SEMEVAL.

[22]  Bo Pang,et al.  Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales , 2005, ACL.

[23]  Takashi Inui,et al.  Extracting Semantic Orientations of Phrases from Dictionary , 2007, NAACL.

[24]  Peter D. Turney.  Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL , 2001, ECML.

[25]  Aliza Sarlan,et al.  Twitter sentiment analysis , 2014, Proceedings of the 6th International Conference on Information Technology and Multimedia.

[26]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[27]  Philip S. Yu,et al.  A holistic lexicon-based approach to opinion mining , 2008, WSDM '08.

[28]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[29]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[30]  Bing Liu,et al.  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.

[31]  Patrick Paroubek,et al.  Twitter for Sentiment Analysis: When Language Resources are Not Available , 2011, 2011 22nd International Workshop on Database and Expert Systems Applications.

[32]  Ted Pedersen,et al.  WordNet::Similarity - Measuring the Relatedness of Concepts , 2004, NAACL.

[33]  Janyce Wiebe,et al.  Just How Mad Are You? Finding Strong and Weak Opinion Clauses , 2004, AAAI.

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

[35]  Lei Zhang,et al.  Identifying Noun Product Features that Imply Opinions , 2011, ACL.

[36]  Kathleen R. McKeown,et al.  Predicting the semantic orientation of adjectives , 1997 .

[37]  Janyce Wiebe,et al.  Learning Subjective Adjectives from Corpora , 2000, AAAI/IAAI.

[38]  Eric K. Ringger,et al.  Pulse: Mining Customer Opinions from Free Text , 2005, IDA.

[39]  Carlo Strapparava,et al.  Corpus-based and Knowledge-based Measures of Text Semantic Similarity , 2006, AAAI.

[40]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.