Paper Info
Title
A formal study of classification techniques on entity discovery and their application to opinion mining
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.
Year
DOI
Venue
2010
10.1145/1871985.1871992
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Keywords
Field
DocType
linguistic feature,entity discovery,online product review,various classification technique,classification task,opinion mining,opinion mining task,formal study,good classification performance,classification technique,classification performance,sentiment analysis,part of speech
Data science,Data mining,Information retrieval,Computer science,Sentiment analysis,Product reviews
Conference
Citations 
PageRank 
References 
0
0.34
14
Authors
4
Name
Order
Citations
PageRank
Shadi Banitaan1479.14
Saeed Salem218217.39
Wei Jin337031.30
Ibrahim Aljarah470333.62