Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews

In this paper, we dedicate to the topic of aspect ranking, which aims to automatically identify important product aspects from online consumer reviews. The important aspects are identified according to two observations: (a) the important aspects of a product are usually commented by a large number of consumers; and (b) consumers' opinions on the important aspects greatly influence their overall opinions on the product. In particular, given consumer reviews of a product, we first identify the product aspects by a shallow dependency parser and determine consumers' opinions on these aspects via a sentiment classifier. We then develop an aspect ranking algorithm to identify the important aspects by simultaneously considering the aspect frequency and the influence of consumers' opinions given to each aspect on their overall opinions. The experimental results on 11 popular products in four domains demonstrate the effectiveness of our approach. We further apply the aspect ranking results to the application of document-level sentiment classification, and improve the performance significantly.

[1]  Malik Yousef,et al.  One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..

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

[3]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

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

[5]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[6]  Victoria S. Uren,et al.  Building and applying a concept hierarchy representation of a user profile , 2003, SIGIR.

[7]  Trevor Hastie,et al.  An exploration of sentiment summarization , 2003 .

[8]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

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

[10]  Nigel Collier,et al.  Sentiment Analysis using Support Vector Machines with Diverse Information Sources , 2004, EMNLP.

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

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

[13]  Doug Downey,et al.  Unsupervised named-entity extraction from the Web: An experimental study , 2005, Artif. Intell..

[14]  Giuseppe Carenini,et al.  Extracting knowledge from evaluative text , 2005, K-CAP '05.

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

[16]  Janyce Wiebe,et al.  Annotating Attributions and Private States , 2005, FCA@ACL.

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

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

[19]  Xiaoyan Zhu,et al.  Movie review mining and summarization , 2006, CIKM '06.

[20]  Xiaojin Zhu,et al.  Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization , 2006 .

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

[22]  Jackie Chi Kit Cheung,et al.  Multi-Document Summarization of Evaluative Text , 2013, EACL.

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

[24]  Regina Barzilay,et al.  Multiple Aspect Ranking Using the Good Grief Algorithm , 2007, NAACL.

[25]  Guy Lapalme,et al.  Query-Based Summarization of Customer Reviews , 2007, Canadian Conference on AI.

[26]  Xu Ling,et al.  Topic sentiment mixture: modeling facets and opinions in weblogs , 2007, WWW '07.

[27]  Mike Wells,et al.  Structured Models for Fine-to-Coarse Sentiment Analysis , 2007, ACL.

[28]  Xinying Xu,et al.  Hidden sentiment association in chinese web opinion mining , 2008, WWW.

[29]  Ivan Titov,et al.  A Joint Model of Text and Aspect Ratings for Sentiment Summarization , 2008, ACL.

[30]  Vikas Sindhwani,et al.  Document-Word Co-regularization for Semi-supervised Sentiment Analysis , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[31]  Sasha Blair-Goldensohn,et al.  Sentiment Summarization: Evaluating and Learning User Preferences , 2009, EACL.

[32]  B. Liu,et al.  Expanding Domain Sentiment Lexicon through Double Propagation , 2009, IJCAI.

[33]  Tao Li,et al.  A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge , 2009, ACL.

[34]  Bruno Ohana,et al.  Sentiment Classification of Reviews Using SentiWordNet , 2009 .

[35]  Jong C. Park,et al.  Toward finer-grained sentiment identification in product reviews through linguistic and ontological analyses , 2009, ACL/IJCNLP.

[36]  Vincent Ng,et al.  Mine the Easy, Classify the Hard: A Semi-Supervised Approach to Automatic Sentiment Classification , 2009, ACL.

[37]  Yue Lu,et al.  Rated aspect summarization of short comments , 2009, WWW '09.

[38]  Jungi Kim,et al.  Discovering the Discriminative Views: Measuring Term Weights for Sentiment Analysis , 2009, ACL/IJCNLP.

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

[40]  Lise Getoor,et al.  Opinion Graphs for Polarity and Discourse Classification , 2009, Graph-based Methods for Natural Language Processing.

[41]  Xuanjing Huang,et al.  Phrase Dependency Parsing for Opinion Mining , 2009, EMNLP.

[42]  Jingbo Zhu,et al.  Aspect-based sentence segmentation for sentiment summarization , 2009, CIKM 2009.

[43]  Ji Fang,et al.  Pruning Non-Informative Text Through Non-Expert Annotations to Improve Aspect-Level Sentiment Classification , 2010, PWNLP@COLING.

[44]  Tejashri Inadarchand Jain,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2010 .

[45]  Christopher Joseph Pal,et al.  Cross Lingual Adaptation: An Experiment on Sentiment Classifications , 2010, ACL.

[46]  Mike Thelwall,et al.  A Study of Information Retrieval Weighting Schemes for Sentiment Analysis , 2010, ACL.

[47]  Alok N. Choudhary,et al.  Voice of the Customers: Mining Online Customer Reviews for Product Feature-based Ranking , 2010, WOSN.

[48]  Yue Lu,et al.  Latent aspect rating analysis on review text data: a rating regression approach , 2010, KDD.

[49]  Takaaki Hasegawa,et al.  Optimizing Informativeness and Readability for Sentiment Summarization , 2010, ACL.

[50]  Claire Cardie,et al.  Hierarchical Sequential Learning for Extracting Opinions and Their Attributes , 2010, ACL.

[51]  Kam-Fai Wong,et al.  A Unified Graph Model for Sentence-Based Opinion Retrieval , 2010, ACL.

[52]  Iryna Gurevych,et al.  Sentence and Expression Level Annotation of Opinions in User-Generated Discourse , 2010, ACL.

[53]  Nitin Indurkhya,et al.  Handbook of Natural Language Processing , 2010 .

[54]  R. Bodin.  A Sentimental Education , 2011 .