A formal study of classification techniques on entity discovery and their application to opinion mining

Authors:
Shadi Banitaan;Saeed Salem;Wei Jin;Ibrahim Aljarah
Affiliations:
North Dakota State University, Fargo, USA;North Dakota State University, Fargo, USA;North Dakota State University, Fargo, USA;North Dakota State University, Fargo, USA
Venue:
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Year:
2010

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Abstract

Entity discovery has become an important topic of study in recent years due to its wide range of applications. In this paper, we focus on examining the effectiveness of various classification techniques on entity discovery and their application to the opinion mining task. The initial and most important step in opinion mining is to identify and extract highly specific product related and opinion related entities from product reviews. We formulate this problem as a classification task and present a comprehensive study of classification techniques on identifying entities of interest. The impacts of linguistic features such as part-of-speech (POS), and context features such as surrounding contextual clues of words on the classification performance are carefully evaluated. The experimental results show that good classification performance is closely related to the use of classification techniques, linguistic features, and context features. The evaluation is presented based on processing the online product reviews from Amazon.