Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble.

ABSTRACT: The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features. The model employs the Cascaded Feature Selection (CFS) strategy, a novel st

SUBMITTER: Yenkikar A 

PROVIDER: S-EPMC9575864 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature