A new hierarchy framework for feature engineering through multi‐objective evolutionary algorithm in text classification

Abstract

Sentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special topic like a product, an organization, a person, or an incident. With the growth of user-generated content on the Web, this field gained great importance in online reviews. With a wide range of reviews, customers cannot read all reviews. Considering the increasing rate of electronic documents and the urgent need manually mine for keywords that are hard and time-consuming, doing the same automatically is of high demand. A new framework proposed here to mine and classify users' comments based on mining keywords by applying the sequence pattern mining through the Separation-Power concept, a multi-objective evolutionary algorithm based on decomposition with four objectives, and a neural network as the final classifier. Some modifications are made on multi-objective evolutionary algorithm based on decomposition and Apriori algorithms to improve the text classification efficiency. To evaluate the proposed framework, three datasets applied; which compared with the two methods to measure accuracy, precision, recall, and error-index. The results indicate that this framework provides a better outcome than its counterparts with 99.45 precision, 99.34 accuracy, 99.48 recall, and 99.28% f-measure.

Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques

“…Authors applied decision boundaries in cross-domain sentiment analysis ( Fu & Liu, 2022 ). Authors mine keywords and applied feature engineering techniques to explore patterns ( Asgarnezhad, Monadjemi & Aghaei, 2022 ). Aspect-level sentiment analysis has been performed using an adaptive SVM model and Twitter dataset ( Liu et al, 2022 ).…”