Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
A maximum entropy approach to natural language processing
Computational Linguistics
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Extracting semantic orientations of words using spin model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Emotion classification using massive examples extracted from the web
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A blog emotion corpus for emotional expression analysis in Chinese
Computer Speech and Language
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Emotion ontology construction from chinese knowledge
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Proceedings of the 21st ACM international conference on Information and knowledge management
Unsupervised Emotion Detection from Text Using Semantic and Syntactic Relations
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Chinese emotion lexicon developing via multi-lingual lexical resources integration
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Sentiment topic models for social emotion mining
Information Sciences: an International Journal
| Hi-index | 0.00 |
Emotion words have been well used as the most obvious choice as feature in the task of textual emotion recognition and automatic emotion lexicon construction. In this work, we explore features for recognizing word emotion. Based on Ren-CECps (an annotated emotion corpus) and MaxEnt (Maximum entropy) model, several contextual features and their combination have been experimented. Then PLSA (probabilistic latent semantic analysis) is used to get semantic feature by clustering words and sentences. The experimental results demonstrate the effectiveness of using semantic feature for word emotion recognition. After that, "word emotion components" is proposed to describe the combined basic emotions in a word. A significant performance improvement over contextual and semantic features was observed after adding word emotion components as feature.