Abstract: Millions of users share opinions on various topics using micro-blogging every day. Twitter is a very popular micro-blogging site where users are allowed a limit of 140 characters; this kind of restriction makes the users be concise as well as expressive at the same time. For that reason, it becomes a rich source for sentiment analysis and belief mining. The aim of this paper is to develop such a functional classifier which can correctly and automatically classify the sentiment of an unknown tweet. In our work, we propose techniques to classify the sentiment label accurately. We introduce two methods: one of the methods is known as sentiment classification algorithm (SCA) based on k-nearest neighbor (KNN) and the other one is based on support vector machine (SVM). We also evaluate their performance based on real tweets.

Mohammad Rezwanul Huq, Ahmad Ali and Anika Rahman, “Sentiment Analysis on Twitter Data using KNN and SVM” International Journal of Advanced Computer Science and Applications(IJACSA), 8(6), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080603

@article{Huq2017,
title = {Sentiment Analysis on Twitter Data using KNN and SVM},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080603},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080603},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {6},
author = {Mohammad Rezwanul Huq and Ahmad Ali and Anika Rahman}
}