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 classi er for the task of sentiment classi cation. We systematically analyze four di fferent English and three di erent 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 classi cation 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},
}
