Summary of the paper
| Title | Quantising Opinions for Political Tweets Analysis |
|---|---|
| Authors | Yulan He, Hassan Saif, Zhongyu Wei and Kam-Fai Wong |
| Abstract | There have been increasing interests in recent years in analyzing tweet messages relevant to political events so as to understand public opinions towards certain political issues. We analyzed tweet messages crawled during the eight weeks leading to the UK General Election in May 2010 and found that activities at Twitter is not necessarily a good predictor of popularity of political parties. We then proceed to propose a statistical model for sentiment detection with side information such as emoticons and hash tags implying tweet polarities being incorporated. Our results show that sentiment analysis based on a simple keyword matching against a sentiment lexicon or a supervised classifier trained with distant supervision does not correlate well with the actual election results. However, using our proposed statistical model for sentiment analysis, we were able to map the public opinion in Twitter with the actual offline sentiment in real world. |
| Topics | Text mining, Document Classification, Text categorisation, Emotion Recognition/Generation |
Full paper  |
Quantising Opinions for Political Tweets Analysis |
| Bibtex | @InProceedings{HE12.217, author = {Yulan He and Hassan Saif and Zhongyu Wei and Kam-Fai Wong}, title = {Quantising Opinions for Political Tweets Analysis}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } |