Thus, we adopted user bias as the basis for building accurate classification models. This approach works because while new terms may arise and old terms may change their meaning, user bias tends to be more consistent over time as a basic property of human behavior. We then analyze sentiments by transferring user biases to textual features. Instead of learning textual models to predict content polarity (i.e., the traditional sentiment analysis approach), we first measure the bias of social media users toward a topic, by solving a relational learning task over a network of users connected by endorsements (e.g., retweets in Twitter). We identify a task – opinion holder bias prediction – which is strongly related to the sentiment analysis task however, in constrast to sentiment analysis, it builds accurate models since the underlying relational data follows a stationary distribution. In this paper, we propose a transfer learning strategy to perform real time sentiment analysis. However, this task comes with several challenges, including the need to deal with highly dynamic textual content that is characterized by changes in vocabulary and its subjective meaning and the lack of labeled data needed to support supervised classifiers. In such an environment, the ability to automatically analyze user opinions and sentiments as discussions develop is a powerful resource known as real time sentiment analysis. Real-time interaction, which enables live discussions, has become a key feature of most Web applications. Ó 2014 Production and hosting by Elsevier B.V. The main contributions of this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas. The main target of this survey is to give nearly full image of SA techniques and the related fields with brief details.
The related fields to SA (transfer learning, emotion detection, and building resources) that attracted researchers recently are discussed.
These articles are categorized according to their contributions in the various SA techniques. Many recently proposed algorithms' enhancements and various SA applications are investigated and presented briefly in this survey. This survey paper tackles a comprehensive overview of the last update in this field. SA is the computational treatment of opinions, sentiments and subjectivity of text. Sentiment Analysis (SA) is an ongoing field of research in text mining field.
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