Twitter Sentiment Mining: A Multi Domain Analysis

Microblogging such as Twitter provides a rich source of information about products, personalities, and trends, etc. We proposed a simple methodology for analyzing sentiment of users in Twitter. First, we automatically collected Twitter corpus in positive and negative tweets. Second, we built a simple sentiment classifier by utilizing the Naive Bayes model to determine the positive and negative sentiment of a tweet. Third, we tested the classifier against a collection of users' opinions from five interesting domains of Twitter, i.e., news, finance, job, movies, and sport. The experimental results show that it is feasible to use Twitter corpus alone to classify new tweet for a certain domain applications.

[1]  Vasileios Hatzivassiloglou,et al.  Predicting the Semantic Orientation of Adjectives , 1997, ACL.

[2]  Khairullah Khan,et al.  Mining opinion from text documents: A survey , 2009, 2009 3rd IEEE International Conference on Digital Ecosystems and Technologies.

[3]  George A. Miller,et al.  Introduction to WordNet: An On-line Lexical Database , 1990 .

[4]  Sabine Bergler,et al.  Mining WordNet for a Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses , 2006, EACL.

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

[6]  François-Régis Chaumartin,et al.  UPAR7: A knowledge-based system for headline sentiment tagging , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

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

[8]  Carlo Strapparava,et al.  WordNet Affect: an Affective Extension of WordNet , 2004, LREC.

[9]  Mitsuru Ishizuka,et al.  Emotion Estimation and Reasoning Based on Affective Textual Interaction , 2005, ACII.

[10]  Hazem M. Hajj,et al.  A Framework for Emotion Mining from Text in Online Social Networks , 2010, 2010 IEEE International Conference on Data Mining Workshops.

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

[12]  Cecilia Ovesdotter Alm,et al.  Emotions from Text: Machine Learning for Text-based Emotion Prediction , 2005, HLT.

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

[14]  Janyce Wiebe,et al.  Just How Mad Are You? Finding Strong and Weak Opinion Clauses , 2004, AAAI.

[15]  Thomas Hofmann,et al.  Text categorization by boosting automatically extracted concepts , 2003, SIGIR.

[16]  Claire Cardie,et al.  Recognizing and Organizing Opinions Expressed in the World Press , 2003, New Directions in Question Answering.

[17]  P. Ekman.  An argument for basic emotions , 1992 .

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

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

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

[21]  Prem Melville,et al.  Sentiment analysis of blogs by combining lexical knowledge with text classification , 2009, KDD.

[22]  Bei Yu,et al.  Exploring the characteristics of opinion expressions for political opinion classification , 2008, DG.O.