Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/9980
Title: The Role of Text Pre-processing in Sentiment Analysis
Authors: Haddi, E
Liu, X
Shi, Y
Keywords: Science & Technology;Technology;Computer Science, Information Systems;Computer Science, Theory & Methods;Computer Science;Sentiment Analysis;Text Pre-processing;Feature Selection;Chi Squared;SVM;SUPPORT VECTOR MACHINES;SELECTION
Issue Date: 2013
Publisher: ELSEVIER SCIENCE BV
Citation: 1st International Conference on Information Technology and Quantitative Management (ITQM), Suzhou, PEOPLES R CHINA, 17: 26 - 32, 2013-05-16 - 2013-05-18
Abstract: It is challenging to understand the latest trends and summarise the state or general opinions about products due to the big diversity and size of social media data, and this creates the need of automated and real time opinion extraction and mining. Mining online opinion is a form of sentiment analysis that is treated as a difficult text classification task. In this paper, we explore the role of text pre-processing in sentiment analysis, and report on experimental results that demonstrate that with appropriate feature selection and representation, sentiment analysis accuracies using support vector machines (SVM) in this area may be significantly improved. The level of accuracy achieved is shown to be comparable to the ones achieved in topic categorisation although sentiment analysis is considered to be a much harder problem in the literature.
URI: http://bura.brunel.ac.uk/handle/2438/9980
DOI: http://dx.doi.org/10.1016/j.procs.2013.05.005
ISSN: 1877-0509
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf373.81 kBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.