Improving aspect extraction by augmenting a frequency-based method with web-based similarity measures

Abstract Online review mining has been used to help manufacturers and service providers improve their products and services, and to provide valuable support for consumer decision making. Product aspect extraction is fundamental to online review mining. This research is aimed to improve the performance of aspect extraction from online consumer reviews. To this end, we augment a frequency-based extraction method with PMI-IR, which utilizes web search in measuring the semantic similarity between aspect candidates and target entities. In addition, we extend RCut, an algorithm originally developed for text classification, to learn the threshold for selecting candidate aspects. Experiment results with Chinese online reviews show that our proposed method not only outperforms the state of the art frequency-based method for aspect extraction but also generalizes across different product domains and various data sizes.

[1]  Irene Pollach,et al.  Electronic Word of Mouth: A Genre Analysis of Product Reviews on Consumer Opinion Web Sites , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[2]  Alice H. Oh,et al.  Aspect and sentiment unification model for online review analysis , 2011, WSDM '11.

[3]  Kuiyu Chang,et al.  Mining Chinese Reviews , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[4]  Wendy G. Lehnert,et al.  Information extraction , 1996, CACM.

[5]  Eric Chang,et al.  Red Opal: product-feature scoring from reviews , 2007, EC '07.

[6]  R. Peterson,et al.  Taking the pulse of Internet pharmacies. , 2001, Marketing health services.

[7]  R Law,et al.  Mining features of products from Chinese customer online reviews , 2009 .

[8]  Chun Chen,et al.  Opinion Word Expansion and Target Extraction through Double Propagation , 2011, CL.

[9]  David B. Dunson,et al.  Probabilistic topic models , 2011, KDD '11 Tutorials.

[10]  Li Shi,et al.  Improving the performance of features extraction from Chinese customer reviews , 2010, 2010 Second International Conference on Communication Systems, Networks and Applications.

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

[12]  Meng Wang,et al.  Domain-Assisted Product Aspect Hierarchy Generation: Towards Hierarchical Organization of Unstructured Consumer Reviews , 2011, EMNLP.

[13]  Shafiq R. Joty,et al.  Dialogue Act Recognition in Synchronous and Asynchronous Conversations , 2013, SIGDIAL Conference.

[14]  Ivan Titov,et al.  Modeling online reviews with multi-grain topic models , 2008, WWW.

[15]  Robin T. Peterson,et al.  The Quality Dimensions of Internet Retail Food Purchasing , 2002 .

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

[17]  Stephanie Seneff,et al.  Review Sentiment Scoring via a Parse-and-Paraphrase Paradigm , 2009, EMNLP.

[18]  Iryna Gurevych,et al.  Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields , 2010, EMNLP.

[19]  Michael L. Littman,et al.  Measuring praise and criticism: Inference of semantic orientation from association , 2003, TOIS.

[20]  Rohini K. Srihari,et al.  OpinionMiner: a novel machine learning system for web opinion mining and extraction , 2009, KDD.

[21]  Thomas Hofmann,et al.  Probabilistic latent semantic indexing , 1999, SIGIR '99.

[22]  Oren Etzioni,et al.  OPINE: Extracting Product Features and Opinions from Reviews , 2005, HLT/EMNLP.

[23]  Suk Hwan Lim,et al.  Extracting and Ranking Product Features in Opinion Documents , 2010, COLING.

[24]  Brant Barton.  Ratings, Reviews & ROI , 2006 .

[25]  Yiming Yang,et al.  A study of thresholding strategies for text categorization , 2001, SIGIR '01.

[26]  Jian Liu,et al.  Opinion Searching in Multi-Product Reviews , 2006, The Sixth IEEE International Conference on Computer and Information Technology (CIT'06).

[27]  Shi Li,et al.  Research on Infrequent Features Extraction from Chinese Reviews , 2011 .

[28]  Ming Zhou,et al.  Low-Quality Product Review Detection in Opinion Summarization , 2007, EMNLP.

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

[30]  Maria T. Pazienza,et al.  Information Extraction , 2002, Lecture Notes in Computer Science.

[31]  Mark Levene,et al.  An Introduction to Search Engines and Web Navigation (2. ed.) , 2005 .

[32]  J. Keziya Rani,et al.  Mining Opinion Features in Customer Reviews. , 2016 .

[33]  Yuesi Wang,et al.  Distribution and sources of solvent extractable organic compounds in PM2.5 during 2007 Chinese Spring Festival in Beijing. , 2009, Journal of environmental sciences.

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

[35]  Pat Lochungvu.  Chiang Mai, Thailand , 2012, The Statesman’s Yearbook Companion.

[36]  Wu Yuming.  Acquiring Part-Whole Relation from the Web , 2013 .

[37]  Vasudeva Varma,et al.  Domain Independent Model for Product Attribute Extraction from User Reviews using Wikipedia , 2011, IJCNLP.

[38]  Xiaotie Deng,et al.  Exploiting Topic based Twitter Sentiment for Stock Prediction , 2013, ACL.

[39]  P. Bhattacharyya,et al.  Aspect Based Sentiment Analysis-A Survey , 2017 .

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

[41]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[42]  Ioannis Pavlopoulos,et al.  Aspect based sentiment analysis , 2014 .

[43]  Chu-Ren Huang,et al.  Extracting Chinese Product Features: Representing a Sequence by a Set of Skip-Bigrams , 2012, CLSW.

[44]  Sasha Blair-Goldensohn,et al.  Building a Sentiment Summarizer for Local Service Reviews , 2008 .

[45]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[46]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[47]  Chuanming Yu,et al.  Mining Product Features from Free-Text Customer Reviews: An SVM-Based Approach , 2009, 2009 First International Conference on Information Science and Engineering.

[48]  Iryna Gurevych,et al.  A Comparative Study of Feature Extraction Algorithms in Customer Reviews , 2008, 2008 IEEE International Conference on Semantic Computing.