New Clustering Algorithms for Twitter Sentiment Analysis

Abstract

In this last decade, the use of social networks became ubiquitous in our daily life. Twitter, one of the famous social networks became a rich source of discussed topics. The users in Twitter express their sentiments or points of view by tweets concerning different topics in variety of fields, such as politics, commercial products, etc. These important information are exploited by sentiment analysis tools. Clustering algorithms are one of the used solutions to discover the sentiment provided by users in tweets. However, knowing that the users sentiments are generally divided into three categories: positive, negative, and neutral, it was mandatory to find a strong clustering algorithm, which leads to a good clustering performance and produce an appropriate number of clusters in an acceptable run time. To achieve this goal, we combine in this paper two well-known clustering methods: K-means and DENCLUE (DENsity-based CLUstEring) with its variants. This combination allows to exploit the precise number of cluster from K-means and the clustering performance from DENCLUE and its variants. Experimental results on four Twitter datasets demonstrate the competitiveness of the proposed algorithms against the state-of-the-art methods to provide a tradeoff between clustering performance, number of returned clusters, and runtime.