Sentence to Document Level Emotion Tagging – A Coarse-Grained Study on Bengali Blogs

This paper presents the identification of document level emotions from the sentential emotions obtained at word level granularity. Each of the Bengali blog documents consists of a topic and corresponding user comments. Sense weight based average scoring technique for assigning sentential emotion tag follows the word level emotion tagging using Support Vector Machine (SVM) approach. Cumulative summation of sentential emotion scores is assigned to each document considering the combinations of some heuristic features. An average F-Score of 59.32% with respect to all emotion classes is achieved on 95 documents on the development set by incorporating the best feature combination into account. Instead of assigning a single emotion tag to a document, each document is assigned with the best two emotion tags according to the ordered emotion scores obtained. The best two system assigned emotion tags of each document are compared against best two human annotated emotion tags. Evaluation of 110 test documents yields an average F-Score of 59.50% with respect to all emotion classes.