Welcome to our project, where we leverage advanced sentiment analysis techniques to detect and classify toxic content in game-related tweets. Our goal is to develop a predictive model that can accurately identify toxicity based on the language used in these tweets. By employing cutting-edge machine learning methods, we have created an efficient and precise classification model.
The outcomes of this project have significant implications for social media platforms, particularly in the gaming industry. Our model's ability to automatically identify toxic content can empower social media companies to take swift action in removing harmful tweets from their platforms. By maintaining a safe and positive environment, these companies can enhance user experiences and foster healthier online communities.
In addition to the machine learning model, we have also developed a user-friendly web application. This platform allows users to conveniently assess the toxicity level of game-related tweets. Built with a Flask backend and utilizing HTML, CSS, and JavaScript for the frontend, our web app provides an intuitive and seamless experience for users.
We are excited to present our project's results, which demonstrate the effectiveness of our advanced sentiment analysis techniques in detecting toxicity within the French game tweets dataset. Through this endeavor, we aim to contribute to the ongoing efforts to combat toxic behavior and promote a more inclusive and enjoyable online gaming community.