ChatRoller was created to address the need for live analytics of chat messages during streaming. As aspiring streamers ourselves, we recognized that there were no tools available that provided real-time sentiment analysis of chat messages. This creates a challenge for streamers, particularly during larger streams, as it can be difficult to keep up with all the messages and engage with viewers. Our application aims to bridge this gap by providing streamers with a quick and easy summary of the chat's overall mood. This helps streamers to stay informed and engaged with their audience, and ultimately make streaming a more enjoyable and interactive experience for both streamers and viewers. We believe that our application will provide a fresh perspective by offering chat analytics that have not been readily available before.
What it does
- ChatRoller uses Cohere's language model to perform real-time sentiment analysis on chat messages during a livestream
- Provides a summary of the overall mood of the chat, making it easier for streamers to keep up with their audience
- Allows streamers to quickly identify any negative or positive trends in the chat, helping them to improve their engagement with viewers
- Provides a unique perspective that is not available through standard viewer analytics, giving streamers a more comprehensive understanding of their audience
How we built it
- Utilized Express to host the application locally
- Leveraged Cohere's language model to process the livestream messages in real-time, analyze their sentiment, and generate a concise summary of the chat messages
- Used the YouTube Data API to get real-time chat messages from the livestream
- Employed Node.js as the server-side runtime environment for our application, allowing us to build scalable and efficient server-side functionality.
Challenges we ran into
During the development of our application, we faced some challenges with API restrictions, such as limited number of API calls per minute with Cohere, and a daily limit on API calls for the YouTube Data API. Additionally, we encountered some issues with the reliability and speed of the Cohere API, which sometimes caused delays in our application's output. We also found that some chat messages were not suitable for analysis, such as one-word or emote-heavy chats in gaming streams, and the Cohere API did not always provide a response for chats with controversial or political topics. Despite these challenges, we were able to develop a functional application that provides valuable insights for streamers and their audiences.
Accomplishments that we're proud of
We are incredibly proud of what we were able to accomplish with our application, especially considering that it was our first time working with APIs and using natural language processing. It was amazing to learn so much about the new and existing technologies we used, and to see firsthand how they could be combined to create something useful for people. We are particularly proud that we were able to create a tool that can effectively analyze and summarize chat messages in real-time, and that can potentially help streamers engage more effectively with their audiences. Overall, we believe that our application represents a significant accomplishment and an important step forward in our journey as developers.
What we learned
ring the development of our application, we learned a great deal about the uses of AI and machine learning in real-world applications. Working with Cohere's language model opened our eyes to the incredible potential of natural language processing and sentiment analysis, and we were able to gain valuable insights into the various ways that machine learning algorithms can be used to process and analyze large amounts of data in real-time. We believe that this experience has given us a greater appreciation for the power of AI and machine learning, and we are excited to continue exploring these technologies in the future.
What's next for ChatRoller
Moving forward, we plan on finishing the implementation of the function that allows users to choose which YouTube livestream chat they want to analyze. We also plan on creating more functions to display additional stats such as the most frequently used words, the percentage of positive, neutral, and negative messages, and other relevant information that will help streamers better understand their audience. These additional features will allow streamers to get a deeper insight into their audience's sentiment and behavior, and help them create a more engaging and interactive stream. We are excited to continue working on this project and exploring the full potential of AI and machine learning in the field of live streaming.
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