tusharsarkar3/TLA
A comprehensive tool for linguistic analysis of communities
This tool helps social scientists, marketers, or community managers understand public opinion by analyzing Twitter data. It takes raw tweets from 16 different languages and processes them to identify the language, extract key topics, and determine the sentiment (positive or negative). The output is labeled datasets and comprehensive statistics on community sentiment, providing insights into various linguistic groups.
No commits in the last 6 months.
Use this if you need to systematically collect, categorize by language, and analyze sentiment from large volumes of Twitter data across multiple global communities.
Not ideal if you need to analyze data from platforms other than Twitter or require highly nuanced sentiment analysis beyond positive/negative classifications.
Stars
49
Forks
9
Language
Python
License
MIT
Category
Last pushed
Oct 01, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/tusharsarkar3/TLA"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
maximtrp/bitermplus
Biterm Topic Model (BTM): modeling topics in short texts
stephenhky/PyShortTextCategorization
Various Algorithms for Short Text Mining
clips/pattern
Web mining module for Python, with tools for scraping, natural language processing, machine...
Hassaan-Elahi/Writing-Styles-Classification-Using-Stylometric-Analysis
✍️ An intelligent system that takes a document and classifies different writing styles within...
eimg/burmese-text-classifier
A neural network based text classification system for Burmese