Who is Who in Identifying Sentiments on a Microblogging Social Network

Abstract

This paper focuses on sentiment analysis in microblogging social networks using natural language processing and machine learning techniques, highlighting the importance of understanding human expressions in the digital world, especially on microblogging platforms where millions of users share opinions and emotions in real-time. The paper proposes a methodology that combines base classifiers, lexical resources, and deep learning techniques to identify and categorize post content. The results show that classifiers like Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees achieve high accuracy in sentiment classification. This study contributes to developing automated tools for extracting information from unstructured texts, improving decision-making based on relevant and accurate data.