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.
Highlights
These days social networks, blogs, and other media produce a huge amount of data on the World Wide Web
One of the technique facilitates k-nearest neighbor (KNN) and the other uses support vector machine (SVM). Both techniques work with same dataset and same features. For both sentiment classification algorithm (SCA) and SVM we calculate weights based on different features
On the other hand in SVM, build a matrix from the calculated weights based on different features and by applying PCA [2], we try to find k eigenvector with the largest Eigen values
Summary
These days social networks, blogs, and other media produce a huge amount of data on the World Wide Web. On the other hand in SVM, build a matrix from the calculated weights based on different features and by applying PCA (principal component analysis) [2], we try to find k eigenvector with the largest Eigen values. From this transformed sample dataset we try to find the best c and best gamma by using grid search technique [3] to use in SVM. We apply SVM to assign the sentiment label of each tweet in the test dataset In both algorithms, we use confusion matrix [4] to calculate the accuracy. We found that Sentiment Classifier Algorithm (SCA) performs better than SVM
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