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Authors: M. Fernandes Caíña ; R. Díaz Redondo and A. Fernández Vilas

Affiliation: University of Vigo, Spain

ISBN: 978-989-758-048-2

Keyword(s): Opinion Mining, Sentiment Analysis, Business Decision-Making, Longitudinal Analysis.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Intelligence Applications ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Pre-Processing and Post-Processing for Data Mining ; Soft Computing ; Symbolic Systems ; Web Mining

Abstract: Social networks have become a major source of information, opinions and sentiments about almost any subject. The purpose of this work is to provide evidences of the applicability of opinion mining methods to find out how some events may impact into public opinion about a brand, product or service. We report an experiment that mined Twitter data related to two particular brands during specific periods that have been selected from events that was supposed to affect the user’s perception. To find out conclusions, the methodology of the experiment applies several pre-processing techniques to extract sentiment information from the posts (e.g., case alterations, Part-of-Speech tagging using a Natural Language Toolkit, symbols removal, sentence and n-gram separation). The SenticNet 2 Corpus is used for polarity classification by means of a supervised algorithm where several threshold values are defined to mark positive, negative and neutral opinions. A longitudinal inspection of the polarize d results on histograms allows identifying the "hot spots" and relating them to real world events. Although this paper shows the finding in our initial experiments, the ultimate goal of the research initiative, which we call Marble, is to provide a cloud solution for early detection of opinion shifts by the automatic classification of events according to their impact on opinion (propagation speed, intensity and duration), and its relationship with the normal behavior around a brand, product or service. (More)

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Paper citation in several formats:
Fernandes Caíña, M.; Díaz Redondo, R. and Fernández Vilas, A. (2014). Marble Initiative - Monitoring the Impact of Events on Customers Opinion.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 403-410. DOI: 10.5220/0005151504030410

@conference{kdir14,
author={M. Fernandes Caíña. and R. Díaz Redondo. and A. Fernández Vilas.},
title={Marble Initiative - Monitoring the Impact of Events on Customers Opinion},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={403-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005151504030410},
isbn={978-989-758-048-2},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - Marble Initiative - Monitoring the Impact of Events on Customers Opinion
SN - 978-989-758-048-2
AU - Fernandes Caíña, M.
AU - Díaz Redondo, R.
AU - Fernández Vilas, A.
PY - 2014
SP - 403
EP - 410
DO - 10.5220/0005151504030410

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