Learning to identify emotions in text
Proceedings of the 2008 ACM symposium on Applied computing
Proceedings of the first workshop on Online social networks
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Micro-blogging as online word of mouth branding
CHI '09 Extended Abstracts on Human Factors in Computing Systems
A machine learning approach to sentiment analysis in multilingual Web texts
Information Retrieval
Automatic Extraction for Product Feature Words from Comments on the Web
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
Holistic sentiment analysis across languages: multilingual supervised latent Dirichlet allocation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Multilingual subjectivity: are more languages better?
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
SentiFul: A Lexicon for Sentiment Analysis
IEEE Transactions on Affective Computing
Target-dependent Twitter sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Re-tweeting from a linguistic perspective
LSM '12 Proceedings of the Second Workshop on Language in Social Media
TOM: Twitter opinion mining framework using hybrid classification scheme
Decision Support Systems
| Hi-index | 0.00 |
Twitter is a microblogging service where worldwide users publish their feelings. However, sentiment analysis for Twitter messages (tweets) is regarded as a challenging problem because tweets are short and informal. In this paper, we focus on this problem by the analysis of emotion tokens, including emotion symbols (e.g. emoticons), irregular forms of words and combined punctuations. According to our observation on five million tweets, these emotion tokens are commonly used (0.47 emotion tokens per tweet). They directly express one's emotion regardless of his language; hence become a useful signal for sentiment analysis on multilingual tweets. Firstly, emotion tokens are extracted automatically from tweets. Secondly, a graph propagation algorithm is proposed to label the tokens' polarities. Finally, a multilingual sentiment analysis algorithm is introduced. Comparative evaluations are conducted among semantic lexicon based approach and some state-of-the-art Twitter sentiment analysis Web services, both on English and non-English tweets. Experimental results show effectiveness of the proposed algorithms.