
Sentiment Analysis Reloaded: A Comparative Study On Sentiment Polarity Identification Combining Machine Learning And Subjectivity Features
Title: Sentiment Analysis Reloaded: A Comparative Study On Sentiment Polarity Identification Combining Machine Learning And Subjectivity Features
Authors: Ulli Waltinger
Pub/Conf: Web Information Systems and Technologies – 6th International Conference, WEBIST 2010, Valencia, Spain, April 7-10, 2010
Abstract:
In recent years a variety of approaches in classifying the sentiment polarity of texts have been proposed. While in the majority of approaches the determination of subjectivity or polarity-related term features is at the center, the number of publicly available dictionaries is rather limited. In this paper, we investigate the performance of combining lexical resources with machine learning-based classier for the task of sentiment classication. We systematically analyze four different English and three dierent German polarity dictionaries as a resources for a sentiment-based feature selection. The evaluation results show that smaller but more controlled dictionaries used for feature selection perform within a SVM-based classication setup equally good compared to the biggest available resources.
BibTeX:
@inproceedings{DBLP:conf/webist/Waltinger10, author = {Ulli Waltinger}, title = {Sentiment Analysis Reloaded - A Comparative Study on Sentiment Polarity Identification Combining Machine Learning and Subjectivity Features}, booktitle = {WEBIST 2010, Proceedings of the 6th International Conference on Web Information Systems and Technologies, Volume 1, Valencia, Spain, April 7-10, 2010}, publisher = {INSTICC Press}, year = {2010}, isbn = {978-989-674-025-2}, pages = {203-210}, }