Welcome to Arabica’s documentation!¶
Arabica is a python library for exploratory data analysis specifically designed for time-series text data. It reflects the reality that many text datasets are now collected as repeated observations over time (social media conversations, research metadata, product reviews, newspaper headlines, central bankers’ communication, etc.).
Descriptive n-gram analysis: n-gram frequencies
Time-series n-gram analysis: n-gram frequencies over a period
Text visualization: n-gram heatmap, line plot, word cloud
Sentiment analysis: VADER sentiment classifier
Financial sentiment analysis: with FinVADER
Structural breaks identification: Jenks Optimisation Method
N-grams are continuous sequences of words in a document. Technically, they are the neighboring sequences of items in a text. Some examples include:
unigram: “dog”, bigram: “dog, goes”, trigram: “dog, goes, home”
unigram: “flower”, bigram: “flower, grows”, trigram: “flower, grows, here”
Contents¶
I have created this project in my free time, and I hope Arabica will save you some time. You can invite me for coffe if Arabica helps you with your project, thesis, or research paper.