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Authors: Jean-Valère Cossu 1 and Liana Ermakova 2

Affiliations: 1 Vodkaster and MyLI - My Local Influence, France ; 2 Université Paris-Est Marne-la-Vallée and Université de Lorraine, France

ISBN: 978-989-758-248-6

Keyword(s): Online Reputation Monitoring, Topic Categorization, Contextualization, Query Expansion, Natural Language Processing, Information Retrieval, Tweet, Classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Collaborative Computing ; Enterprise Information Systems ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Society, e-Business and e-Government ; Software Agents and Internet Computing ; Symbolic Systems ; User Profiling and Recommender Systems ; Web 2.0 and Social Networking Controls ; Web Information Systems and Technologies

Abstract: Opinion and trend mining on micro-blogs like Twitter recently attracted research interest in several fields including Information Retrieval (IR) and Natural Language Processing (NLP). However, the performance of existing approaches is limited by the quality of available training material. Moreover, explaining automatic systems’ suggestions for decision support is a difficult task thanks to this lack of data. One of the promising solutions of this issue is the enrichment of textual content using large micro-blog archives or external document collections, e.g. Wikipedia. Despite some advantages in Reputation Dimension Classification (RDC) task pushed by RepLab, it remains a research challenge. In this paper we introduce a supervised classification method for RDC based on a threshold intersection graph. We analyzed the impact of various micro-blogs extension methods on RDC performance. We demonstrated that simple statistical NLP methods that do not require any external resources can be e asily optimized to outperform the state-of-the-art approaches in RDC task. Then, the conducted experiments proved that the micro-blog enrichment by effective expansion techniques can improve classification quality. (More)

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Paper citation in several formats:
Cossu, J.; Ermakova, L. and Ermakova, L. (2017). Lexical Context for Profiling Reputation of Corporate Entities.In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-248-6, pages 567-576. DOI: 10.5220/0006284505670576

@conference{iceis17,
author={Cossu, J. and Liana Ermakova. and Liana Ermakova.},
title={Lexical Context for Profiling Reputation of Corporate Entities},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2017},
pages={567-576},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006284505670576},
isbn={978-989-758-248-6},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Lexical Context for Profiling Reputation of Corporate Entities
SN - 978-989-758-248-6
AU - Cossu, J.
AU - Ermakova, L.
AU - Ermakova, L.
PY - 2017
SP - 567
EP - 576
DO - 10.5220/0006284505670576

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