Machine Learning
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Emotions from text: machine learning for text-based emotion prediction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
What emotions do news articles trigger in their readers?
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Emotion Classification Using Web Blog Corpora
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Word to sentence level emotion tagging for Bengali blogs
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Analysis and tracking of emotions in english and bengali texts: a computational approach
Proceedings of the 20th international conference companion on World wide web
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
| Hi-index | 0.01 |
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.