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Real-time messaging and opinion sharing in social media websites have made them valuable sources of different kinds of information. This source provides the opportunity for doing different kinds of analysis. Sentiment analysis as one of the most important of these analyses gains increasing interests. However, the research in this field is still facing challenges. The mainstream of the sentiment analysis research on social media websites and microblogs just exploits the textual content of the posts. This makes the analysis hard because microblog posts are short and noisy. However, they have lots of contexts which can be exploited for sentiment analysis. In order to use the context as an auxiliary source, some recent papers use reply/retweet to model the context of the target post. We claim that multiple sequential contexts can be used jointly in a unified model. In this article, we propose a context-aware multi-thread hierarchical long short-term memory (MHLSTM) that jointly models different kinds of contexts, such as tweep, hashtag and reply besides the content of the target post. Experimental evaluations on a real-world Twitter data set demonstrate that our proposed model can outperform some strong baseline models by 28.39% in terms of relative error reduction. [ABSTRACT FROM AUTHOR]

This article provides a bibliometric study of the sentiment analysis literature based on Web of Science (WoS) until the end of 2016 to evaluate current research trends, quantitatively and qualitatively. We concentrate on the analysis of scientific documents, distribution of subject categories, languages of documents and languages that have been more investigated in sentiment analysis, most prolific and impactful authors and institutions, venues of publications and their geographic distribution, most cited and hot documents, trends of keywords and future works. Our investigations demonstrate that the most frequent subject categories in this field are computer science, engineering, telecommunications, linguistics, operations research and management science, information science and library science, business and economics, automation and control systems, robotics and social sciences. In addition, the most active venue of publication in this field is Lecture Notes in Computer Science (LNCS). The United States, China and Singapore have the most prolific or impactful institutions. A keyword analysis demonstrates that sentiment analysis is a more accepted term than opinion mining. Twitter is the most used social network for sentiment analysis and Support Vector Machine (SVM) is the most used classification method. We also present the most cited and hot documents in this field and authors' suggestions for future works. [ABSTRACT FROM AUTHOR]