Abstract
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.
Highlights
Predicting individuals’ emotional states based on their written texts and feedback is important, and challenging due to language ambiguity [1]
This paper aims to give a review of different methods of detecting emotions from the text that exists in literature, doing such review could give a global sight of the field, and avoid redundancies, and get an idea of the different existing methods and analysing the opportunity of improvement and innovation
In another study on unsupervised learning methods for emotion detection from text, [18] proposed a new method based on emotional signals
Summary
Predicting individuals’ emotional states based on their written texts and feedback is important, and challenging due to language ambiguity [1]. Most of the time textual expressions are direct using emotional words such as “happy” or “angry”, but emotions can be extracted from the interpretation of the meanings and contexts. The need for emotion detection is increasing while both structured and unstructured data are getting larger because of social media [2], it is still a research area that needs a lot of effort before reaching the success of sentiment analysis. Emotion detection is critical in the human-machine relation. Emotions may be detected from speech, facial expression, and written text. Compared to text-based emotion recognition, a sufficient amount of work was done regarding facial and speech emotion detection
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