RoBERTa and VADER Sentiment Analysis: A Comparative Approach

Social media is very popular worldwide with 4.62 million active users. It has become a part of our daily lives to connect with friends and new people. It will continuously inform us about new trends, and events and keep us updated. It is a platform where we can express ourselves. The advantages of social media are that we can stay connected with family and friends even if they live far away from us. We can share photos, videos, and updates about our daily lifestyle. By using social media we can learn new things through watching videos. Social media is the biggest source of Entertainment. While social media has many advantages, it is important to be aware of the potential risks, such as privacy concerns, spam messages, negative messages, threatening comments, and hate speech that spreads hate among the communities. In this research, we will implement an AI model using the RoBERTa and Vader modes. RoBERTa is a large language model which is very effective in natural language processing tasks such as text classification. Also, Vader is a sentiment analysis model that is useful for identifying the positive and negative sentiments in the text. The models will be trained on a dataset of labeled social media posts. The labels will be useful to indicate whether the post is harmful or not. To overcome these concerns, we implement NLP-powered Thread: WebApp with RoBERTa and VADER using AI models that are RoBERTa and Vader.