Extraction of Unexpected Sentences: A Sentiment Classification Assessed Approach

Haoyuan Li (1, 2) , Anne Laurent (3) , Pascal Poncelet (3) , Mathieu Roche (4)

1 LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 LGI2P - Laboratoire de Génie Informatique et Ingénierie de Production
3 TATOO - Fouille de données environnementales
4 TEXTE - Exploration et exploitation de données textuelles

Sentiment classification in text documents is an active data mining research topic in opinion retrieval and analysis. Different from previous studies concentrating on the development of effective classifiers, in this paper, we focus on the extraction and validation of unexpected sentences issued in sentiment classification. In this paper, we propose a general framework for determining unexpected sentences. The relevance of the extracted unexpected sentences is assessed in the context of text classification. In the experiments, we present the extraction of unexpected sentences for sentiment classification within the proposed framework, and then evaluate the influence of unexpected sentences on the quality of classification tasks. The experimental results show the effectiveness and usefulness of our proposed approach.

Mots clés: Sentiment classification; Text classification; Unexpected sentences; Extraction; Validation

Domaines: Recherche d'information [cs.IR]

Haoyuan Li, Anne Laurent, Pascal Poncelet, Mathieu Roche. Extraction of Unexpected Sentences: A Sentiment Classification Assessed Approach. Intelligent Data Analysis, 2010, 14 (1), pp.31-46. ⟨10.3233/IDA-2010-0407⟩. ⟨lirmm-00401363⟩