The Role of Text Pre-processing in Sentiment Analysis | Zendy

E. Haddi | Zendy; Xiaohui Liu | Zendy; Yong Shi | Zendy

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The Role of Text Pre-processing in Sentiment Analysis

Author(s) -

E. Haddi,

Xiaohui Liu,

Yong Shi

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.05.005

Subject(s) - sentiment analysis , computer science , support vector machine , selection (genetic algorithm) , representation (politics) , artificial intelligence , feature selection , task (project management) , social media , information retrieval , natural language processing , big data , data science , machine learning , data mining , world wide web , management , politics , political science , law , economics

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

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