Detecting emotions from text is an extension of simple sentiment polarity detection. Instead of considering only positive or negative sentiments, emotions are conveyed using more tangible manner; thus, they can be expressed as many shades of gray. This paper manifests the results of our experimentation for fine-grained emotion analysis on Bangla text. We gathered and annotated a text corpus consisting of user comments from several Facebook groups regarding socio-economic and political issues, and we made efforts to extract the basic emotions (sadness, happiness, disgust, surprise, fear, anger) conveyed through these comments. Finally, we compared the results of the five most popular classical machine learning techniques namely Naive Bayes, Decision Tree, k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and K-Means Clustering with several combinations of features. Our best model (SVM with a non-linear radial-basis function (RBF) kernel) achieved an overall average accuracy score of 52.98 and an F1 score (macro) of 0.3324
Rapid growth of blogs in the Web 2.0 and the handshaking between multilingual search and sentiment analysis motivate us to develop a blog based emotion analysis system for Bengali. The present paper describes the identification, visualization and tracking of bloggers' emotions with respect to time from Bengali blog documents. A simple pre-processing technique has been employed to retrieve and store the bloggers' comments on specific topics. The assignment of Ekman's six basic emotions to the bloggers' comments is carried out at word, sentence and paragraph level granularities using the Bengali WordNet AffectLists. The evaluation produces the precision, recall and F-Score of 59.36 , 64.98 and 62.17 respectively for 1100 emotional comments retrieved from 20 blog documents. Each of the bloggers' emotions with respect to different timestamps is visualized by an emotion graph. The emotion graphs of 20 bloggers demonstrate that the system performs satisfactorily in case of emotion tracking.