Target-Dependent Sentiment Analysis for Hashtags on Twitter

Microblogging services, such as Twitter, allow Internet users to exchange short messages easily. Users express their feelings on various topics as status messages or opinions. Hashtags are usually used in these services to mark essential words or phrases as a means for grouping topics. Thus, sentiment analysis on hashtags has become a popular method in determining user opinions on microblogs. In this paper, an effective approach to determine target-dependent hashtag sentiments is proposed. For a given tweet, a hashtag may carry different or even opposite opinions for different targets. Therefore, we aim to identify sentiment for two-dimensional parameters, namely < hashtag, target >. We firstly build a target-dependent tweet-level sentiment classifier based on target position sensitive features. A majority voting strategy for hashtag-level sentiment classification is then proposed as a baseline method. Finally, we show that its performance is significantly improved by propagation on a hyper relationship graph containing both target and hashtag nodes.

[1]  W. Freeman,et al.  Generalized Belief Propagation , 2000, NIPS.

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

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

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

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

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

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

[8]  Bing Liu,et al.  Identifying comparative sentences in text documents , 2006, SIGIR.

[9]  Bing Liu,et al.  Mining Comparative Sentences and Relations , 2006, AAAI.

[10]  S. Kotsiantis Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

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

[12]  Susumu Horiguchi,et al.  Learning to classify short and sparse text & web with hidden topics from large-scale data collections , 2008, WWW.

[13]  Sarabjot S. Anand,et al.  Predicting the Polarity Strength of Adjectives Using WordNet , 2009, ICWSM.

[14]  Janyce Wiebe,et al.  Articles: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis , 2009, CL.

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

[16]  Ari Rappoport,et al.  Semi-Supervised Recognition of Sarcasm in Twitter and Amazon , 2010, CoNLL.

[17]  Ari Rappoport,et al.  ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews , 2010, ICWSM.

[18]  Junlan Feng,et al.  Robust Sentiment Detection on Twitter from Biased and Noisy Data , 2010, COLING.

[19]  Ari Rappoport,et al.  Enhanced Sentiment Learning Using Twitter Hashtags and Smileys , 2010, COLING.

[20]  Hakan Ferhatosmanoglu,et al.  Short text classification in twitter to improve information filtering , 2010, SIGIR.

[21]  Xiaolong Wang,et al.  Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach , 2011, CIKM '11.

[22]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[23]  Pushpak Bhattacharyya,et al.  C-Feel-It: A Sentiment Analyzer for Micro-blogs , 2011, ACL.

[24]  Tiejun Zhao,et al.  Target-dependent Twitter Sentiment Classification , 2011, ACL.

[25]  Youngjoong Ko,et al.  Extracting Comparative Entities and Predicates from Texts Using Comparative Type Classification , 2011, ACL.

[26]  Pushpak Bhattacharyya,et al.  TwiSent: a multistage system for analyzing sentiment in twitter , 2012, CIKM '12.