Summary of the paper
| Title | Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition |
|---|---|
| Authors | Yi-jie Tang and Hsin-Hsi Chen |
| Abstract | The conversations between posters and repliers in microblogs form a valuable writer-reader emotion corpus. This paper adopts a log relative frequency ratio to investigate the linguistic features which affect emotion transitions, and applies the results to predict writers' and readers' emotions. A 4-class emotion transition predictor, a 2-class writer emotion predictor, and a 2-class reader emotion predictor are proposed and compared. |
| Topics | Emotion Recognition/Generation, Lexicon, lexical database, Text mining |
Full paper  |
Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition |
| Bibtex | @InProceedings{TANG12.117, author = {Yi-jie Tang and Hsin-Hsi Chen}, title = {Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition}, 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} } |