This project is a basic content classifier designed to classify descriptions of webtoons, anime, and K-dramas into different categories such as Romance, Action, Fantasy, Mystery, and Adventure. The classifier takes in textual descriptions and categorizes them based on the themes present in the content.
Text Classification: Uses machine learning to classify descriptions of anime, K-dramas, and webtoons into specific categories. Predefined Categories: The current categories include Romance, Action, Fantasy, Mystery, and Adventure. Simple Interface: A straightforward script that can be easily expanded to classify more categories or content.
Python Scikit-learn (for text classification)
Pandas (for handling datasets)
This project is a Sentiment Analysis API integrated with a Large Language Model (LLM). The API analyzes input text (such as product reviews or social media posts) and classifies them as Positive, Negative, or Neutral sentiments.
Sentiment Classification: The API takes user input text and determines its sentiment. LLM Integration: Utilizes an LLM for natural language understanding and enhanced sentiment accuracy. Simple REST API: Easy-to-use endpoints for sending text data and receiving sentiment analysis.
Python
Pandas (for data handling)
Scikit-learn (for basic machine learning models)
This project is a simple knowledge-based chatbot designed to answer questions about the Castle Swimmer webtoon. It uses NLTK for text processing and responds to specific questions about the webtoon's plot, characters, and themes.
Knowledge Base: Predefined information about Castle Swimmer, including characters, plot, and themes.
Streamlit Integration: A simple web interface using Streamlit to interact with the chatbot.
Text Processing: Uses NLTK for tokenizing and understanding user input.
Python
NLTK (Natural Language Toolkit)
Streamlit (for UI)