SENTIMENT RATING ALGORITHM OF PRODUCT ONLINE REVIEWS

Social media give new opportunities in customer and market survey to improve design; comments posted online by users spontaneously, in a near-oral language is almost free of biases. This new source however has huge size, so complexity of data needs to be processed. In this paper, we propose an automated method to process these comments into a sentiment rating useful for future designs. We validate it on the example of a commercial home theatre system, comparing our automated sentiment predictions with human ratings on a group of 15 test subjects, resulting in a satisfactory correlation.

[1]  Stephanie W. Haas.  Natural Language Processing: Toward Large-Scale, Robust Systems. , 1996 .

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

[3]  Elizabeth D. Liddy,et al.  Enhanced Text Retrieval Using Natural Language Processing , 2005 .

[4]  Douglas W. Oard,et al.  Cross-language Information Retrieval , 2021, ArXiv.

[5]  Xin Liu,et al.  Generic text summarization using relevance measure and latent semantic analysis , 2001, SIGIR '01.

[6]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[7]  Barbara J. Grosz,et al.  Natural-Language Processing , 1982, Artificial Intelligence.

[8]  Cynthia Whissell,et al.  THE DICTIONARY OF AFFECT IN LANGUAGE , 1989 .

[9]  Ted Pedersen,et al.  Knowledge Lean Word-Sense Disambiguation , 1997, AAAI/IAAI.

[10]  Tao Li,et al.  The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering , 2006, Sixth International Conference on Data Mining (ICDM'06).

[11]  M McGue,et al.  Genetic and environmental influences on human behavioral differences. , 1998, Annual review of neuroscience.

[12]  Lucila Ohno-Machado,et al.  Natural language processing: an introduction , 2011, J. Am. Medical Informatics Assoc..

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

[14]  Sun Park,et al.  Automatic generic document summarization based on non-negative matrix factorization , 2009, Inf. Process. Manag..

[15]  G. Somprasertsri,et al.  A maximum entropy model for product feature extraction in online customer reviews , 2008, 2008 IEEE Conference on Cybernetics and Intelligent Systems.

[16]  Stefan M. Wild,et al.  Motivating non-negative matrix factorizations , 2003 .

[17]  Srinivas Bangalore,et al.  Supertagging: An Approach to Almost Parsing , 1999, CL.

[18]  Tomaz Erjavec,et al.  Learning Word Segmentation Rules for Tag Prediction , 1999, ILP.

[19]  Dan Klein,et al.  Accurate Unlexicalized Parsing , 2003, ACL.

[20]  Dan Klein,et al.  Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network , 2003, NAACL.

[21]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

[22]  Johan Bollen,et al.  Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena , 2009, ICWSM.

[23]  Shlomo Argamon,et al.  A Memory-Based Approach to Learning Shallow Natural Language Patterns , 1998, ACL.

[24]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

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

[26]  R. A. Amsler.  Machine-readable dictionaries , 1984 .

[27]  Mor Naaman,et al.  Is it really about me?: message content in social awareness streams , 2010, CSCW '10.

[28]  Kazuhiko Tsuda,et al.  The extraction method of the word meaning class , 1999, 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Proceedings (Cat. No.99TH8410).

[29]  Holley R. Lange.  Speech Synthesis and Speech Recognition: Tomorrow's Human-Computer Interfaces?. , 1993 .

[30]  Ken Barker,et al.  Using Noun Phrase Heads to Extract Document Keyphrases , 2000, Canadian Conference on AI.