Unsupervised sentiment analysis with emotional signals

The explosion of social media services presents a great opportunity to understand the sentiment of the public via analyzing its large-scale and opinion-rich data. In social media, it is easy to amass vast quantities of unlabeled data, but very costly to obtain sentiment labels, which makes unsupervised sentiment analysis essential for various applications. It is challenging for traditional lexicon-based unsupervised methods due to the fact that expressions in social media are unstructured, informal, and fast-evolving. Emoticons and product ratings are examples of emotional signals that are associated with sentiments expressed in posts or words. Inspired by the wide availability of emotional signals in social media, we propose to study the problem of unsupervised sentiment analysis with emotional signals. In particular, we investigate whether the signals can potentially help sentiment analysis by providing a unified way to model two main categories of emotional signals, i.e., emotion indication and emotion correlation. We further incorporate the signals into an unsupervised learning framework for sentiment analysis. In the experiment, we compare the proposed framework with the state-of-the-art methods on two Twitter datasets and empirically evaluate our proposed framework to gain a deep understanding of the effects of emotional signals.

[1]  Chris H. Q. Ding,et al.  Convex and Semi-Nonnegative Matrix Factorizations , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

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

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

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

[7]  Brendan T. O'Connor,et al.  From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.

[8]  Yue Lu,et al.  Automatic construction of a context-aware sentiment lexicon: an optimization approach , 2011, WWW.

[9]  Quanquan Gu,et al.  Co-clustering on manifolds , 2009, KDD.

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

[11]  Nicholas Diakopoulos,et al.  Cooooooooooooooollllllllllllll!!!!!!!!!!!!!! Using Word Lengthening to Detect Sentiment in Microblogs , 2011, EMNLP.

[12]  David A. Shamma,et al.  Tweet the debates: understanding community annotation of uncollected sources , 2009, WSM@MM.

[13]  Lei Zhang,et al.  A Survey of Opinion Mining and Sentiment Analysis , 2012, Mining Text Data.

[14]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

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

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

[17]  Fei Wang,et al.  What Were the Tweets About? Topical Associations between Public Events and Twitter Feeds , 2012, ICWSM.

[18]  Minyi Guo,et al.  Emoticon Smoothed Language Models for Twitter Sentiment Analysis , 2012, AAAI.

[19]  Kiran Bhowmick,et al.  A Survey of Opinion Mining and Sentiment Analysis , 2015 .

[20]  Marshall S. Smith,et al.  The general inquirer: A computer approach to content analysis. , 1967 .

[21]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, Web Intelligence.

[22]  E. Huffman,et al.  Social Media's New Role in Emergency Management , 2008 .

[23]  Wei Peng,et al.  Generate Adjective Sentiment Dictionary for Social Media Sentiment Analysis Using Constrained Nonnegative Matrix Factorization , 2021, ICWSM.

[24]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[25]  Ke Xu,et al.  MoodLens: an emoticon-based sentiment analysis system for chinese tweets , 2012, KDD.

[26]  Ralf Reski,et al.  High frequency of phenotypic deviations in Physcomitrella patens plants transformed with a gene-disruption library , 2002, BMC Plant Biology.

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

[28]  Nan Sun,et al.  Exploiting internal and external semantics for the clustering of short texts using world knowledge , 2009, CIKM.

[29]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[30]  Wiltrud Kessler.  Turney: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classication of Reviews , 2012 .

[31]  Chris H. Q. Ding,et al.  Orthogonal nonnegative matrix t-factorizations for clustering , 2006, KDD '06.

[32]  Chris H. Q. Ding,et al.  Bridging Domains with Words: Opinion Analysis with Matrix Tri-factorizations , 2010, SDM.

[33]  Shelly Farnham,et al.  Whoo.ly: facilitating information seeking for hyperlocal communities using social media , 2013, CHI.

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

[35]  Huan Liu,et al.  Exploiting homophily effect for trust prediction , 2013, WSDM.

[36]  Claire Cardie,et al.  Annotating Expressions of Opinions and Emotions in Language , 2005, Lang. Resour. Evaluation.

[37]  Huan Liu,et al.  Exploiting social relations for sentiment analysis in microblogging , 2013, WSDM.

[38]  Alok N. Choudhary,et al.  Graphical Modeling of Macro Behavioral Targeting in Social Networks , 2013, SDM.

[39]  R. Abelson.  Whatever Became of Consistency Theory? , 1983 .

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

[41]  Jon Oberlander,et al.  IN PROCEEDINGS OF EACL-2006 , 2006 .