Emotion Detection in Text: Advances in Sentiment Analysis Using Deep Learning

Abstract

In the modern era of digital communication, the analysis of sentiment has emerged as a crucial tool for understanding and inferring public sentiment as communicated through written text. This is particularly relevant in the context of social media platforms such as Twitter, Facebook and Instagram. The present study focuses on the urgent matter of public opinion regarding the practice of animal testing, employing advanced deep-learning methodologies for sentiment analysis. A dataset of 15,360 tweets about animal testing was collected using the Twitter API. The data was prepared for analysis by undergoing careful preprocessing and word embedding it through the utilization of Word2vec. To classify tweets into positive and negative sentiment categories, a Long Short-Term Memory (LSTM) model was employed, given its suitability for processing sequential data. Remarkably, an accuracy rate of 88.7 percent was achieved by the model. It was determined that around 80% of tweets expressed criticism towards animal testing, indicating the presence of a substantial negative sentiment majority. These results show the topic's continuing significance by emphasizing its highly emotional and controversial nature. It is concluded that deep learning, and in particular LSTM models, can be used to effectively analyze large amounts of social media data and yield insightful understandings of public opinion. This study underlines the significance of sentiment analysis for gaining insight into public opinion and for its applications in policymaking and discourse analysis.