Open Access Open Access  Restricted Access Subscription or Fee Access

Emotion Classification of Twitter Data using Lexicon based Approach

M. Reka, Dr. V. Srividhya


The main aim of sentiment analysis is to determine the attitude of a speaker or a writer with respect to some topic. The sentiment classification has been classified into two types which are emotional classification and polarity classification. This research work has been done by using emotional classification, which is used to classify the emotions such as joy, fear, disgust, anger, sad and surprise. These six types of emotions are classified using twitter dataset. The classified emotions are visualized using graph. The work is focused on analyzing the tweets of people for Donald Trump and Hillary Clinton and classifies the sentiment from tweets.


Emotion; Sentiment Analysis; Naïve Bayesian Algorithm; Lexicon; Word Cloud; Visualization.

Full Text:



Veeramani.S, Karuppusamy.S “A Survey on SentimentAnalysis Technique in Web Opinion Mining” International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Volume 3 Issue 8, August 2014.

Yanghui Rao, Qing Li, Xudong Mao,Liu Wenyin “Sentimenttopic model for social emotion mining”, Elsevier publication,2014.

Liu, B. (2012) Sentiment Analysis and Opinion Mining .Morgan & Claypool Publishers

A. Farzindar and D. Inkpen, Natural language processing for social media,Synthesis Lectures on Human Lang. Tech., vol. 8, no.2, pp. 1{166, Sept. 2015

Bhuvaneswari.M and Srividhya.V, “Enhancing the Sentiment Classification accuracy of twitter data using Sentiment Classification”, Internationl Journal of Engineering Research publication Vol.5,sep-oct 2016 .

Falguni N. Patel, Neha R. Soni,” Text mining: A Brief survey”, International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-2 Number- 4 Issue-6 December-2012.

Po-Wei Liang, Bi-Ru Dai, “Opinion Mining on Social MediaData", IEEE 14th International Conference on Mobile Data Management,Milan, Italy, June 3 - 6, 2013, pp 91-96,ISBN:978-1-494673-6068-5,

Go, R. Bhayani, L.Huang. “Twitter Sentiment ClassificationUsing Distant Supervision". Stanford University, Technical Paper, 2009.

Rischan Mafrur, M Fiqri Muthohar, Gi Hyun Bang, Do Kyeong Lee, Kyungbaek Kim and Deokjai Choi “Twitter Mining: The Case of 2014 Indonesian Legislative Elections” International Journal of Software Engineering and Its Applications Vol. 8, No. 10 (2014), pp. 191-202.

R. Xia, C. Zong, and S. Li, “Ensemble of feature sets and classification algorithms for sentiment classification,” Information Sciences: an International Journal, vol. 181, no. 6, pp. 1138–1152, 2011.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.