Combining naive bayes and adjective analysis for sentiment detection on Twitter

Twitter is an emerging platform to express the opinion on various issues. Plenty of approaches like machine learning, information retrieval and NLP have been exercised to figure out the sentiment of the tweets. We have used movie reviews as our data set for training as well as testing and merged the naive bayes and adjective analysis for finding the polarity of the ambiguous tweets. Experimental outputs reveal that the overall accuracy of the process is improved using this model. Firstly we have applied naive bayes on collected tweets which results in set of truly polarized and falsely polarized tweets. False polarized set is further processed with adjective analysis to determine the polarity of tweets and classify it to be positive or negative.

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

[2]  Songbo Tan,et al.  A survey on sentiment detection of reviews , 2009, Expert Syst. Appl..

[3]  Franco Salvetti,et al.  Automatic Opinion Polarity Classification of Movie Reviews , 2004 .

[4]  Trevor J. Hastie,et al.  The Sentimental Factor: Improving Review Classification Via Human-Provided Information , 2004, ACL.

[5]  Pablo Gamallo,et al.  Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets , 2014, *SEMEVAL.

[6]  Soo-Min Kim,et al.  Determining the Sentiment of Opinions , 2004, COLING.

[7]  Naoki Kimura,et al.  An emotion-processing system based on fuzzy inference and its subjective observations , 1994, Int. J. Approx. Reason..

[8]  Pushpak Bhattacharyya,et al.  Adjective Intensity and Sentiment Analysis , 2015, EMNLP.

[9]  Ngoc Thang Vu,et al.  CIS-positive: Combining Convolutional Neural Networks and SVMs for Sentiment Analysis in Twitter , 2015 .

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

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

[12]  Claire Cardie,et al.  OpinionFinder: A System for Subjectivity Analysis , 2005, HLT.

[13]  Long-Sheng Chen,et al.  Journal of Informetrics , 2022 .

[14]  Nigel Collier,et al.  Sentiment Analysis using Support Vector Machines with Diverse Information Sources , 2004, EMNLP.

[15]  Vaibhavi N Patodkar,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2016 .

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

[17]  David Zimbra,et al.  Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network , 2013, Expert Syst. Appl..

[18]  Hiroshi Kanayama,et al.  Deeper Sentiment Analysis Using Machine Translation Technology , 2004, COLING.

[19]  Hiroya Takamura,et al.  Sentiment Classification Using Word Sub-sequences and Dependency Sub-trees , 2005, PAKDD.

[20]  Bernard J. Jansen,et al.  Twitter power: Tweets as electronic word of mouth , 2009, J. Assoc. Inf. Sci. Technol..

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

[22]  Saif Mohammad,et al.  NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets , 2013, *SEMEVAL.

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