Abstract
Twitter is one of the most popular microblogging and social networking platforms where massive instant messages (i.e. tweets) are posted every day. Twitter sentiment analysis tackles the problem of analyzing users’ tweets in terms of thoughts, interests and opinions in a variety of contexts and domains. Such analysis can be valuable for several researchers and applications that require understanding people views about a particular topic or event. The study carried out in this paper provides an overview of the algorithms and approaches that have been used for sentiment analysis in twitter. The reviewed articles are categories into four categories based on the approach they use. Furthermore, we discuss directions for future research on how twitter sentiment analysis approaches can utilize theories and technologies from other fields such cognitive science, semantic Web, big data and visualization.
Highlights
Nowadays, social media platforms like twitter or facebook have gained high importance for many readers as they allow people to share and express their opinions about topics and post messages across the world in a simple way [1]
Ego analysis was applied by analyzing centrality of nodes which is measured by the degree of the various nodes in the graph with degree representing the number of other nodes to which a node is adjacent
Experimental results on a reallife data set consisting of 29195 tweets and 2181 hashtags showed the effectiveness of the proposed hashtag graph model
Summary
Social media platforms like twitter or facebook have gained high importance for many readers as they allow people to share and express their opinions about topics and post messages across the world in a simple way [1]. Analyzing twitter feeds for sentiment analysis has become a major research and business activity [2]. Twitter Sentiment Analysis (TSA) tackles the problem of analyzing the tweets in terms of the opinion they express [3]. Analyzing sentiments is a challenging task due to the vast amount tweets with various topics. This encouraged researches in the field to develop approaches that can automatically detect and mine sentiments opinions within a huge amount of data [3]. There are numerous of articles focused on social media data analysis and, more recently, researchers increased their focus on TSA.
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