Improving sentiment analysis using preprocessing techniques and lexical patterns Online publication date: Fri, 08-Oct-2021
by Stefano Cagnoni; Laura Ferrari; Paolo Fornacciari; Monica Mordonini; Laura Sani; Michele Tomaiuolo
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 13, No. 3, 2021
Abstract: Sentiment analysis has recently gained considerable attention, since the classification of the emotional content of a text (online reviews, blog messages etc.) may have a relevant impact on market research, political science and many other fields. In this paper, we focus on the importance of the text preprocessing phase, proposing a new technique we termed lexical pattern-based feature weighting (LPFW) that allows one to improve sentence-level sentiment analysis by increasing the relevance of the features contained in particular lexical patterns. This approach has been evaluated on two sentiment classification datasets. We show that a systematic optimisation of the preprocessing filters is important for obtaining good classification accuracy. Also, we show that LPFW is effective in different application domains and with different training set sizes.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook