arabica 1.1.1

Python package for exploratory text data analysis

Arabica

Python package for exploratory text data analysis

Text data is often recorded as a time series with significant variability over time. Some examples of time-series text data include Twitter tweets, product reviews, and newspaper headlines. Arabica provides functions to make the exploratory analysis of such datasets simple.

Arabica provides these methods:

• arabica_freq: calculates unigram, bigram, and trigram frequencies over a period (year, month, day)

It can apply all or a selected combination of the following cleaning operations:

• Remove digits from the text
• Remove punctuation from the text
• Remove standard list of stopwords
• Remove an additional specific list of words

Arabica works with texts of languages based on the Latin alphabet, uses clean-text for punctuation cleaning, and enables stop words removal for languages in the NLTK corpus of stopwords.

It reads dates in standard date and datetime formats (e.g., 2013–12–31, 2013/12/31, 09-Feb-2009, 2013–12–31 11:46:17, 09/02/2009 09:26). It is preferable to use the US-style dates (MM/DD/YYYY) rather than the European-style date format (DD/MM/YYYY).

Installation

Arabica requires Python >=3.7, NLTK, clean-text, and numpy to execute. To install using pip, use:

pip install arabica

Usage

• Import the library:

from arabica import arabica_freq

• Choose a method:

arabica_freq returns a dataframe with aggregated unigrams, bigrams, and trigrams frequencies over a period. To remove stopwords, select aggregation period, and choose a specific set of cleaning operations:

def arabica_freq(text: str,                # Text
                 time: str,                # Time
                 time_freq: str ='',       # Aggregation period: 'Y'/'M'/'D', if no aggregation: 'ungroup'
                 max_words: int ='',       # Max number for unigrams, bigrams and trigrams displayed
                 stopwords: [],            # Languages for stop words
                 skip: [],                 # Remove additional strings
                 numbers: bool = False     # Remove all digits
                 punct: bool = False,      # Remove all punctuation
                 lower_case: bool = False, # Lowercase text before cleaning and frequency analysis
) 

A list of available languages for stopwords is printed with:

from nltk.corpus import stopwords
print(stopwords.fileids())

It is possible to remove more sets of stopwords at once by stopwords = ['language 1', 'language2','etc..']

Examples

Time-series n-gram analysis

Returns a table with unigram, bigram, and trigram frequencies over a period of time.

import pandas as pd
from arabica import arabica_freq

data = pd.DataFrame({'text': ['The ordering process was very easy & straight forward. They have great customer service and sorted any issues out very quickly.',
                              'So far seems to be the wrong product for me :-/ grrrrr...',
                              'Excellent, service, thank you really, really, really much!!!'],
                     'time': ['2013-08-8', '2013-09-8','2014-10-8']})

arabica_freq(text = data['text'],
             time = data['time'],
             time_freq = 'M',           # Calculates monthly n-gram frequencies
             max_words = 2,             # Displays two most frequent unigrams, bigrams, and trigrams
             stopwords = ['english'],   # Removes English set of stopwords
             skip = ['grrrrr'],         # Excludes string from n-gram calculation
             numbers = True,            # Removes numbers
             punct = True,              # Removes punctuation
             lower_case = True)         # Lowercase text before cleaning and n-gram calculation  

Descriptive n-gram analysis

Returns unigram, bigram, and trigram frequencies without period aggregation.

arabica_freq(text = data['text'],
             time = data['time'],
             time_freq = 'ungroup',        # No aggregation made
             max_words = 2,
             stopwords = ['english'],
             skip = ['grrrrr'],       
             numbers = True,
             punct = True
             lower_case = True)

Documentation and tutorials

Read the documentation here. For more examples of coding, read this tutorial:

Text as Time Series: Arabica 1.0.0 Brings New Features for Exploratory Text Data Analysis here

License

MIT

For any questions, issues, bugs, and suggestions, please visit here.