The automatic identification of stop words
Journal of Information Science
Machine Learning
An empirical study of sentiment analysis for chinese documents
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Emotion classification using massive examples extracted from the web
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
UPAR7: a knowledge-based system for headline sentiment tagging
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Sentiment analysis of Chinese documents: From sentence to document level
Journal of the American Society for Information Science and Technology
A text-driven rule-based system for emotion cause detection
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Hierarchical versus flat classification of emotions in text
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Ontology-based sentiment analysis of twitter posts
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In recent years, increasing impact of social networks on people's opinions and decision making has attracted lots of attention. Microblogging, one of the most popular social network applications that allows people to share ideas and discuss over various topics, is taken as a rich resource of opinion and emotion data. In this paper, we propose and implement a novel method for identifying emotions in microblog posts. Unlike traditional approaches which are mostly based on statistical methods, we try to infer and extract the reasons of emotions by importing knowledge and theories from other fields such as Sociology. Based on the theory that a triggering cause event is an integral part of emotion, the technique of emotion cause extraction is used as a crucial step to improve the quality of selected features. First, after thorough analysis on sample data we constructed an automatic rule-based system to detect and extract the cause event of each emotional post. We build an emotion corpus with Chinese microblog posts labeled by human annotators. Then a classifier is trained to classify emotions in microblog posts based on extracted cause events. The overall performance of our system is very promising. The experiment results show that our approach is effective in selecting informative features. Our system outperformed the baseline noticeably in most cases, suggesting its great potential. This exploration should provide a new way to look at the emotion classification task and lay the ground for future research on textual emotion processing.