PYTHIA: Employing Lexical and Semantic Features for Sentiment Analysis
Abstract
Sentiment analysis methods aim at identifying the polarity of a piece of text, e.g., passage, review, snippet, by analyzing lexical features at the level of the terms or the sentences. However, many of the previous works do not utilize features that can offer a deeper understanding of the text, e.g., negation phrases. In this work we demonstrate a novel piece of software, namely PYTHIA^1, which combines semantic and lexical features at the term and sentence level and integrates them into machine learning models in order to predict the polarity of the input text. Experimental evaluation of PYTHIA in a benchmark movie reviews dataset shows that the suggested combination performs favorably against previous related methods. An online demo is publicly available at http://omiotis.hua.gr/pythia .
Cite
Text
Katakis et al. "PYTHIA: Employing Lexical and Semantic Features for Sentiment Analysis." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44845-8_32Markdown
[Katakis et al. "PYTHIA: Employing Lexical and Semantic Features for Sentiment Analysis." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/katakis2014ecmlpkdd-pythia/) doi:10.1007/978-3-662-44845-8_32BibTeX
@inproceedings{katakis2014ecmlpkdd-pythia,
title = {{PYTHIA: Employing Lexical and Semantic Features for Sentiment Analysis}},
author = {Katakis, Ioannis Manousos and Varlamis, Iraklis and Tsatsaronis, George},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2014},
pages = {448-451},
doi = {10.1007/978-3-662-44845-8_32},
url = {https://mlanthology.org/ecmlpkdd/2014/katakis2014ecmlpkdd-pythia/}
}