Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Topic identification for fine-grained opinion analysis
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Identifying expressions of emotion in text
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Collecting evaluative expressions for opinion extraction
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Emotion holder for emotional verbs – the role of subject and syntax
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Analysis and tracking of emotions in english and bengali texts: a computational approach
Proceedings of the 20th international conference companion on World wide web
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This paper presents a supervised multi-engine classifier approach followed by voting to identify emotion topic(s) from English blog sentences. Manual annotation of the English blog sentences in the training set has shown a satisfactory agreement with kappa (κ) measure of 0.85 and MASI (Measure of Agreement on Set-valued Items) measure of 0.82 for emotion topic spans. The baseline system based on object related dependency relations includes the topic oriented thematic roles present in the verb based syntactic frame of the sentences. In contrast, the supervised approach consists of three classifiers, Conditional Random Field (CRF), Support Vector Machine (SVM) and a Fuzzy Classifier (FC). The important features are incorporated based on the ablation study of all features and Information Gain Based Pruning (IGBP) on the development set. One or more emotion topics associated with focused target span are identified based on the majority voting of the classifiers. The supervised multi-engine classifier system has been evaluated with average F-scores of 70.51% and 90.44% for emotion topic and target span identification respectively on 500 gold standard test sentences and has outperformed the baseline system.