Summary of the paper
| Title | Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools |
|---|---|
| Authors | Mark Cieliebak, Oliver Dürr and Fatih Uzdilli |
| Abstract | In this paper, we analyze the quality of several commercial tools for sentiment detection. All tools are tested on nearly 30,000 short texts from various sources, such as tweets, news, reviews etc. The best commercial tools have average accuracy of 60%. We then apply machine learning techniques (Random Forests) to combine all tools, and show that this results in a meta-classifier that improves the overall performance significantly. |
| Topics | Opinion Mining / Sentiment Analysis, Emotion Recognition/Generation |
Full paper  |
Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools |
| Bibtex | @InProceedings{CIELIEBAK14.820, author = {Mark Cieliebak and Oliver Dürr and Fatih Uzdilli}, title = {Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |