Year: 2016
Type: conference-paper
Abstract: In this research, the well-known microblogging site, Twitter, was used for a sentiment analysis investigation. We propose an ensemble learning approach based on the meta-level features of seven existing lexicon resources for automated polarity sentiment classification. The ensemble employs four base learners (a Two-Class Support Vector Machine, a Two-Class Bayes Point Machine, a Two-Class Logistic Regression and a Two-Class Decision Forest) for the classification task. Three different labelled Twitter datasets were used to evaluate the effectiveness of this approach to sentiment analysis. Our experiment shows that, based on a combination of existing lexicon resources, the ensemble learners minimize the error rate by avoiding poor selection from stand-alone classifiers.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2594035236