A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Identifying sources of opinions with conditional random fields and extraction patterns
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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
Multi-perspective question answering using the OpQA corpus
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Learning to identify emotions in text
Proceedings of the 2008 ACM symposium on Applied computing
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SWAT-MP: the SemEval-2007 systems for task 5 and task 14
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UA-ZBSA: a headline emotion classification through web information
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UPAR7: a knowledge-based system for headline sentiment tagging
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Identifying expressions of emotion in text
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
AM: textual attitude analysis model
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Affect analysis model: Novel rule-based approach to affect sensing from text
Natural Language Engineering
Speeding up logistic model tree induction
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Hierarchical approach to emotion recognition and classification in texts
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
| Hi-index | 0.00 |
A set of words labelled with their prior emotion is an obvious place to start on the automatic discovery of the emotion of a sentence, but it is clear that context must also be considered. No simple function of the labels on the individual words may capture the overall emotion of the sentence; words are interrelated and they mutually influence their affect-related interpretation. We present a method which enables us to take the contextual emotion of a word and the syntactic structure of the sentence into account to classify sentences by emotion classes. We show that this promising method outperforms both a method based on a Bag-of-Words representation and a system based only on the prior emotions of words. The goal of this work is to distinguish automatically between prior and contextual emotion, with a focus on exploring features important for this task.