A knowledge‐based approach for polarity classification in Twitter

Until now, most of the methods published for polarity classification in Twitter have used a supervised approach. The differences between them are only the features selected and the method used for weighting them. In this article, we present an unsupervised method for polarity classification in Twitter. The method is based on the expansion of the concepts expressed in the tweets through the application of PageRank to WordNet. In addition, we integrate SentiWordNet to compute the final value of polarity. The synsets values are weighted with the PageRank scores obtained in the previous random walk process over WordNet. The results obtained show that disambiguation and expansion are good strategies for improving overall performance.

[1]  Harith Alani,et al.  Semantic Sentiment Analysis of Twitter , 2012, SEMWEB.

[2]  Bernard J. Jansen,et al.  Micro-blogging as online word of mouth branding , 2009, CHI Extended Abstracts.

[3]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[4]  Masatoshi Yoshikawa,et al.  News Bias Analysis Based on Stakeholder Mining , 2011, IEICE Trans. Inf. Syst..

[5]  Rada Mihalcea,et al.  Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity , 2007, International Conference on Semantic Computing (ICSC 2007).

[6]  Philip J. Sallis,et al.  Sentiment-Preserving Reduction for Social Media Analysis , 2011, CIARP.

[7]  Lei Zhang,et al.  Combining lexicon-based and learning-based methods for twitter sentiment analysis , 2011 .

[8]  Dragomir R. Radev,et al.  Identifying Text Polarity Using Random Walks , 2010, ACL.

[9]  Themis Palpanas,et al.  Survey on mining subjective data on the web , 2011, Data Mining and Knowledge Discovery.

[10]  Rada Mihalcea,et al.  Multilingual Subjectivity Analysis Using Machine Translation , 2008, EMNLP.

[11]  Ellen Riloff,et al.  Creating Subjective and Objective Sentence Classifiers from Unannotated Texts , 2005, CICLing.

[12]  Eneko Agirre,et al.  Personalizing PageRank for Word Sense Disambiguation , 2009, EACL.

[13]  Harith Alani,et al.  Automatically Extracting Polarity-Bearing Topics for Cross-Domain Sentiment Classification , 2011, ACL.

[14]  Mike Thelwall,et al.  Sentiment strength detection for the social web , 2012, J. Assoc. Inf. Sci. Technol..

[15]  ThelwallMike,et al.  Sentiment strength detection in short informal text , 2010 .

[16]  David A. Shamma,et al.  Characterizing debate performance via aggregated twitter sentiment , 2010, CHI.

[17]  Akshi Kumar,et al.  Sentiment Analysis on Twitter , 2012 .

[18]  Harith Alani,et al.  Alleviating Data Sparsity for Twitter Sentiment Analysis , 2012, #MSM.

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

[20]  Lluís Padró,et al.  FreeLing 3.0: Towards Wider Multilinguality , 2012, LREC.

[21]  Stéphane Bressan,et al.  A random walk on the red carpet: rating movies with user reviews and pagerank , 2008, CIKM '08.

[22]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[23]  Jason Baldridge,et al.  Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph , 2011, ULNLP@EMNLP.

[24]  M. Bradley,et al.  Affective Normsfor English Words (ANEW): Stimuli, instruction manual and affective ratings (Tech Report C-1) , 1999 .

[25]  Kerstin Denecke,et al.  Using SentiWordNet for multilingual sentiment analysis , 2008, 2008 IEEE 24th International Conference on Data Engineering Workshop.

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

[27]  Mike Thelwall,et al.  Sentiment in Twitter events , 2011, J. Assoc. Inf. Sci. Technol..

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

[29]  Balachander Krishnamurthy,et al.  A few chirps about twitter , 2008, WOSN '08.

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

[31]  Andrea Esuli,et al.  PageRanking WordNet Synsets: An Application to Opinion Mining , 2007, ACL.

[32]  Geoff Hulten,et al.  Mining high-speed data streams , 2000, KDD '00.

[33]  Heiner Stuckenschmidt,et al.  Fine-Grained Sentiment Analysis with Structural Features , 2011, IJCNLP.

[34]  Joel Waldfogel,et al.  Introduction , 2010, Inf. Econ. Policy.

[35]  Albert Bifet,et al.  Sentiment Knowledge Discovery in Twitter Streaming Data , 2010, Discovery Science.

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

[37]  Peter Z. Yeh,et al.  Semantic Interpretation of the Web without the Semantic Web: Toward Business-Aware Web Processors , 2007 .

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

[39]  Qiong Wu,et al.  A two-stage framework for cross-domain sentiment classification , 2011, Expert Syst. Appl..

[40]  Choochart Haruechaiyasak,et al.  Discovering Consumer Insight from Twitter via Sentiment Analysis , 2012, J. Univers. Comput. Sci..

[41]  Cesar. Kim Sang-Woon. Cesar San Martin Sang-Woon Kim . San Martin.  Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Pucón, Chile, November 15-18, 2011. Proceedings , 2011, CIARP.

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

[43]  Martha Palmer,et al.  SemEval-2007 Task-17: English Lexical Sample, SRL and All Words , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

[44]  Yulan He,et al.  Joint sentiment/topic model for sentiment analysis , 2009, CIKM.

[45]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

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

[47]  Owen Rambow,et al.  Sentiment Analysis of Twitter Data , 2011 .

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

[49]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

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

[51]  Chris Mellish,et al.  Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04) , 2004, ACL 2004.

[52]  Luis Alfonso Ureña López,et al.  Improving polarity classification of bilingual parallel corpora combining machine learning and semantic orientation approaches , 2013, J. Assoc. Inf. Sci. Technol..

[53]  Michael E. Lesk,et al.  Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone , 1986, SIGDOC '86.

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

[55]  Rada Mihalcea,et al.  Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling , 2005, HLT.

[56]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL.

[57]  Luis Alfonso Ureña López,et al.  Random Walk Weighting over SentiWordNet for Sentiment Polarity Detection on Twitter , 2012, WASSA@ACL.