loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Giacomo Domeniconi ; Gianluca Moro ; Andrea Pagliarani and Roberto Pasolini

Affiliation: Università degli Studi di Bologna, Italy

ISBN: 978-989-758-158-8

Keyword(s): Transfer Learning, Sentiment Classification, Markov Chain, Parameter Tuning, Language Independence, Opinion Mining.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Clustering and Classification Methods ; Data Analytics ; Data Engineering ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Mining Text and Semi-Structured Data ; Symbolic Systems

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 ac complish 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. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.229.24.100

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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

@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},
}

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.