PetrKorab/Arabica
Python package for text mining of time-series data
Analyze how words and sentiment change over time in text data like social media posts, product reviews, or news headlines. You input a collection of text documents, each with an associated date, and it outputs insights like frequent phrases, sentiment scores, and visual trends. This is useful for researchers, marketers, or analysts tracking public opinion or topic shifts.
No commits in the last 6 months. Available on PyPI.
Use this if you need to understand patterns, sentiment, and significant shifts within a body of text that unfolds over a period, such as customer feedback or financial news.
Not ideal if your text data doesn't have a time component or if you require advanced natural language processing tasks beyond descriptive analysis and sentiment.
Stars
75
Forks
16
Language
Python
License
Apache-2.0
Category
Last pushed
May 01, 2025
Commits (30d)
0
Dependencies
16
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