Sentiment analysis has known a big interest over recent years due to the expansion of data. It has many applications in different fields such as marketing, psychology, human-computer interaction, eLearning, etc. There are many forms of sentiment analysis, namely facial expressions, speech, and text. This article is more interested in sentiment analysis from the text as it is a relatively new field and still needs more effort and research. Sentiment analysis from text is very important for different fields, for eLearning it can be critical in determining the emotional state of students and therefore, putting in place the necessary interactions to motivate students to engage and complete their courses. In this article, we present different methods of sentiment analysis from the text that exist in the literature, beginning from the selection of features or text representation, until the training of the prediction model using either supervised or unsupervised learning algorithms and although there has been so much work done in this domain, there is still effort that can be done to improve the performance and to do that we first need to review the recent methods and approaches put in place on this field and then try to discuss improvements in certain approaches or even proposing new approaches.
Correction to: Guiding the Training of Distributed Text Representation with Supervised Weighting Scheme for Sentiment Analysis
