Sentiment Analysis and Topic Classification with LSTM Networks and TextRazor

Abstract

In the ever-evolving landscape of social media, where user-generated content shapes digital discourse, the need for nuanced sentiment analysis and topic extraction is paramount. This paper presents a comprehensive approach utilizing advanced Natural Language Processing (NLP) techniques to enhance user experience and foster a healthier digital environment. Leveraging Long Short-Term Memory (LSTM) networks for sentiment analysis and TextRazor for topic extraction, the system provides insights into emotional tones and key themes within social media discussions. Through intuitive visualizations, users gain awareness of sentiment trends and topic distributions, empowering informed engagement. The results demonstrate high accuracy in sentiment classification with 86% and effective topic identification, contributing to the mitigation of misinformation and negativity online. This research underscores the potential of advanced NLP methods in cultivating constructive digital spaces and sets the stage for further innovation in the field.