Hybrid Framework for Twitter Data Sentiment Classification using MLP-Deep Learning

Abstract

Organizations collect data on user opinions from social media, enabling research into trends and behavior. Classifying text by identifying sentiments within content is complex due to varying expressions and contexts. Sentence-level analysis, often employed by traditional approaches, overlooks key aspects hidden in the data. A hybrid framework integrating Twitter-based feature selection with sentiment classification using a multi-layer perceptron (MLP) model is introduced in this paper. Techniques such as Support Vector classifiers and Random Forests are examined to enhance the accuracy of classification. Baseline models are compared with the proposed framework, which demonstrates reliable precision and improved classification in experiments across several datasets. The structured hybrid approach directly addresses challenges that arise when analyzing content from social media platforms.