Markov Chain based Method for In-Domain and Cross-Domain Sentiment Classification

Giacomo Domeniconi, Gianluca Moro, Andrea Pagliarani, Roberto Pasolini

2015

Abstract

Sentiment classification of textual opinions in positive, negative or neutral polarity, is a method to understand people thoughts about products, services, persons, organisations, and so on. Interpreting and labelling opportunely text data polarity is a costly activity if performed by human experts. To cut this labelling cost, new cross domain approaches have been developed where the goal is to automatically classify the polarity of an unlabelled target text set of a given domain, for example movie reviews, from a labelled source text set of another domain, such as book reviews. Language heterogeneity between source and target domain is the trickiest issue in cross-domain setting so that a preliminary transfer learning phase is generally required. The best performing techniques addressing this point are generally complex and require onerous parameter tuning each time a new source-target couple is involved. This paper introduces a simpler method based on the Markov chain theory to accomplish both transfer learning and sentiment classification tasks. In fact, this straightforward technique requires a lower parameter calibration effort. Experiments on popular text sets show that our approach achieves performance comparable with other works.

Paper Citation

in Harvard Style

Domeniconi G., Moro G., Pagliarani A. and Pasolini R. (2015). Markov Chain based Method for In-Domain and Cross-Domain Sentiment Classification . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8, pages 127-137. DOI: 10.5220/0005636001270137

in Bibtex Style

@conference{kdir15,
author={Giacomo Domeniconi and Gianluca Moro and Andrea Pagliarani and Roberto Pasolini},
title={Markov Chain based Method for In-Domain and Cross-Domain Sentiment Classification},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},
year={2015},
pages={127-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005636001270137},
isbn={978-989-758-158-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - Markov Chain based Method for In-Domain and Cross-Domain Sentiment Classification
SN - 978-989-758-158-8
AU - Domeniconi G.
AU - Moro G.
AU - Pagliarani A.
AU - Pasolini R.
PY - 2015
SP - 127
EP - 137
DO - 10.5220/0005636001270137