arabica 1.4.7

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, research article metadata, product reviews, and newspaper headlines. Arabica makes exploratory analysis of these time-series text datasets simple by providing:

• 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
• Structural breaks identification: Jenks Optimization Method

It automatically cleans data from punctuation on input. It can also apply all or a selected combination of the following cleaning operations:

• Remove digits from the text
• Remove the standard list(s) of stopwords
• Remove an additional list of specific strings

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

It reads dates in:

• US-style: MM/DD/YYYY (2013-12-31, Feb-09-2009, 2013-12-31 11:46:17, etc.)
• European-style: DD/MM/YYYY (2013-31-12, 09-Feb-2009, 2013-31-12 11:46:17, etc.) date and datetime formats.

Installation

Arabica requires Python 3.8 - 3.10, NLTK - stop words removal, cleantext - text cleaning, wordcloud - word cloud visualization, plotnine - heatmaps and line graphs, matplotlib - word clouds and graphical operations, vaderSentiment - sentiment analysis, and jenskpy for breakpoint identification.

To install using pip, use:

pip install arabica

Usage

• Import the library:

from arabica import arabica_freq
from arabica import cappuccino
from arabica import coffee_break 

• Choose a method:

arabica_freq enables a specific set of cleaning operations (lower casing, numbers, stop words, and additional strings removal) and returns a dataframe with aggregated unigrams, bigrams, and trigrams frequencies over a period.

def arabica_freq(text: str,                # Text
                 time: str,                # Time
                 date_format: str,         # Date format: 'eur' - European, 'us' - American
                 time_freq: str = '',      # Aggregation period: 'Y'/'M'/'D', if no aggregation: 'ungroup'
                 max_words: int = '',      # Maximum of most frequent n-grams displayed for each period
                 stopwords: [],            # Languages for stop words
                 skip: [],                 # Remove additional strings
                 numbers: bool = False,    # Remove all digits
                 lower_case: bool = False  # Lowercase text
) 

cappuccino enables cleaning operations (lower casing, numbers, stop words, and additional strings removal) and provides plots for descriptive (word cloud) and time-series (heatmap, line plot) visualization.

def cappuccino(text: str,                # Text
               time: str,                # Time
               date_format: str,         # Date format: 'eur' - European, 'us' - American
               plot: str = '',           # Chart type: 'wordcloud'/'heatmap'/'line'
               ngram: int = '',          # N-gram size, 1 = unigram, 2 = bigram, 3 = trigram
               time_freq: str = '',      # Aggregation period: 'Y'/'M', if no aggregation: 'ungroup'
               max_words int = '',       # Maximum of most frequent n-grams displayed for each period
               stopwords: [],            # Languages for stop words
               skip: [] ,                # Remove additional strings
               numbers: bool = False,    # Remove numbers
               lower_case: bool = False  # Lowercase text

coffee_break provides sentiment analysis and breakpoint identification in aggregated time series of sentiment.

• The implemented model is VADER (Valence Aware Dictionary and sEntiment Reasoner), a lexicon and rule-based sentiment classifier attuned explicitly to sentiments expressed in social media. See: Hutto, & Gilbert, 2014. VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Available from here.

• Structural breaks in the time series are identified with the Fisher-Jenks algorithm (Jenks, 1977. Optimal data classification for choropleth maps).

def coffee_break(text: str,                 # Text
                 time: str,                 # Time
                 date_format: str,          # Date format: 'eur' - European, 'us' - American
                 skip: [] ,                 # Remove additional strings
                 preprocess: bool = False,  # Clean data from numbers and punctuation
                 time_freq: str ='',        # Aggregation period: 'Y'/'M'
                 n_breaks: int =''          # Number of breaks: min. 2
)

Documentation, examples and tutorials

• Read the documentation

For more examples of coding, read these tutorials:

General use:

• Sentiment Analysis and Structural Breaks in Time-Series Text Data here
• Visualization Module in Arabica Speeds Up Text Data Exploration here
• Text as Time Series: Arabica 1.0 Brings New Features for Exploratory Text Data Analysis here

Applications:

• Business Intelligence: Customer Satisfaction Measurement with N-gram and Sentiment Analysis here
• Meta-data description: TBA

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Please visit here for any questions, issues, bugs, and suggestions.