Open praveenarjun opened 2 hours ago
I am a gssoc contributor and hackbefest
Thank you for your suggestion @praveenarjun! While sentiment analysis can be a valuable feature in many contexts, it may not be the most practical solution for a RAG-based chatbot, as its primary goal is to retrieve relevant information rather than gauge emotional tone.
Instead of solely adding sentiment analysis, I recommend introducing an Input Insights and Summary Module. This module would provide a more comprehensive analysis of user inputs by offering features such as:
Topic Identification: Automatically categorize the topics within user inputs, giving users an overview of the key subjects of the input.
Summarization: Generate concise summaries of longer inputs so users can quickly grasp the context without having to read through everything in detail.
Great it is a great idea I am gonna work on Text summerization
Is your feature request related to a problem? Please describe. I'm always frustrated when I can't easily determine the sentiment of the text extracted from conversations. This makes it difficult to gauge the relevance and emotional tone of the discussions.
Describe the solution you'd like I would like to implement a feature that identifies the sentiment of the extracted text. This feature would analyze the text to determine whether it is positive, negative, or neutral, and display this information to help users understand the relevance and emotional tone of the conversations.
Describe alternatives you've considered An alternative solution could be to integrate an existing sentiment analysis API or library that provides similar functionality. However, building a custom solution could offer more flexibility and better integration with the current system.
Additional context This feature will enhance the user experience by providing insights into the emotional tone of conversations, which can be particularly useful for applications in customer support, social media monitoring, and content analysis.