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Authors: Isidoros Perikos and Ioannis Hatzilygeroudis

Affiliation: University of Patras, Greece

Keyword(s): Sentiment Analysis, Classifier Ensemble, Social Media, Emotion Detection, Text Mining.

Abstract: The advent of social media has changed completely the role of the users and has transformed them from simple passive information seekers to active producers. The user generated textual data in social media and microblogging platforms are rich in emotions, opinions and attitudes and necessitate automated methods to analyse and extract knowledge from them. In this paper, we present a classifier ensemble approach to detect emotional content in social media and examine its performance under bagging and boosting combination methods. The classifier ensemble aims to take advantage of the base classifiers’ benefits and constitutes a promising approach to detect sentiments in social media. Our classifier ensemble combines a knowledge based tool that performs deep analysis of the natural language and two machine learning classifiers, a Naïve Bayes and a Maximum Entropy which are trained on ISEAR and Affective text datasets. The evaluation study conducted revealed quite promising results and in dicates that the ensemble classifier approach can improve the performance of sole classifiers on emotion detection in Twitter and that the boosting seems to be more suitable and to perform better than bagging. (More)

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Paper citation in several formats:
Perikos, I. and Hatzilygeroudis, I. (2016). A Classifier Ensemble Approach to Detect Emotions Polarity in Social Media. In Proceedings of the 12th International Conference on Web Information Systems and Technologies (WEBIST 2016) - Volume 1: SRIS; ISBN 978-989-758-186-1; ISSN 2184-3252, SciTePress, pages 363-370. DOI: 10.5220/0005864503630370

@conference{sris16,
author={Isidoros Perikos. and Ioannis Hatzilygeroudis.},
title={A Classifier Ensemble Approach to Detect Emotions Polarity in Social Media},
booktitle={Proceedings of the 12th International Conference on Web Information Systems and Technologies (WEBIST 2016) - Volume 1: SRIS},
year={2016},
pages={363-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005864503630370},
isbn={978-989-758-186-1},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Web Information Systems and Technologies (WEBIST 2016) - Volume 1: SRIS
TI - A Classifier Ensemble Approach to Detect Emotions Polarity in Social Media
SN - 978-989-758-186-1
IS - 2184-3252
AU - Perikos, I.
AU - Hatzilygeroudis, I.
PY - 2016
SP - 363
EP - 370
DO - 10.5220/0005864503630370
PB - SciTePress

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