loading
Documents

Research.Publish.Connect.

Paper

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

Affiliation: University of Bologna, Italy

ISBN: 978-989-758-271-4

Keyword(s): Transfer Learning, Language Heterogeneity, Sentiment Analysis, Cross-Domain, Big Data.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining Text and Semi-Structured Data ; Soft Computing ; Symbolic Systems

Abstract: Cross-domain sentiment classification consists in distinguishing positive and negative reviews of a target domain by using knowledge extracted and transferred from a heterogeneous source domain. Cross-domain solutions aim at overcoming the costly pre-classification of each new training set by human experts. Despite the potential business relevance of this research thread, the existing ad hoc solutions are still not scalable with real large text sets. Scalable Deep Learning techniques have been effectively applied to in-domain text classification, by training and categorising documents belonging to the same domain. This work analyses the cross-domain efficacy of a well-known unsupervised Deep Learning approach for text mining, called Paragraph Vector, comparing its performance with a method based on Markov Chain developed ad hoc for cross-domain sentiment classification. The experiments show that, once enough data is available for training, Paragraph Vector achieves accuracy equiva lent to Markov Chain both in-domain and cross-domain, despite no explicit transfer learning capability. The outcome suggests that combining Deep Learning with transfer learning techniques could be a breakthrough of ad hoc cross-domain sentiment solutions in big data scenarios. This opinion is confirmed by a really simple multi-source experiment we tried to improve transfer learning, which increases the accuracy of cross-domain sentiment classification. (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 3.81.73.233

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. (2017). On Deep Learning in Cross-Domain Sentiment Classification.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-271-4, pages 50-60. DOI: 10.5220/0006488100500060

@conference{kdir17,
author={Giacomo Domeniconi. and Gianluca Moro. and Andrea Pagliarani. and Roberto Pasolini.},
title={On Deep Learning in Cross-Domain Sentiment Classification},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2017},
pages={50-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006488100500060},
isbn={978-989-758-271-4},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - On Deep Learning in Cross-Domain Sentiment Classification
SN - 978-989-758-271-4
AU - Domeniconi, G.
AU - Moro, G.
AU - Pagliarani, A.
AU - Pasolini, R.
PY - 2017
SP - 50
EP - 60
DO - 10.5220/0006488100500060

Login or register to post comments.

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