MoreThanSentiments

Besides sentiment scores, this Python package offers various ways of quantifying text corpus based on multiple works of literature. Currently, we support the calculation of the following measures:

• Boilerplate (Lang and Stice-Lawrence, 2015)
• Redundancy (Cazier and Pfeiffer, 2015)
• Specificity (Hope et al., 2016)
• Relative_prevalence (Blankespoor, 2016)

A medium blog is here: MoreThanSentiments: A Python Library for Text Quantification

Citation

If this package was helpful in your work, feel free to cite it as

• Jiang, Jinhang, and Karthik Srinivasan. "MoreThanSentiments: A text analysis package." Software Impacts 15 (2023): 100456. https://doi.org/10.1016/J.SIMPA.2022.100456

Installation

The easiest way to install the toolbox is via pip (pip3 in some distributions):

pip install MoreThanSentiments

Usage

Import the Package

import MoreThanSentiments as mts

Read data from txt files

my_dir_path = "D:/YourDataFolder"
df = mts.read_txt_files(PATH = my_dir_path)

Sentence Token

df['sent_tok'] = df.text.apply(mts.sent_tok)

Clean Data

If you want to clean on the sentence level:

df['cleaned_data'] = pd.Series()    
for i in range(len(df['sent_tok'])):
    df['cleaned_data'][i] = [mts.clean_data(x,\
                                            lower = True,\
                                            punctuations = True,\
                                            number = False,\
                                            unicode = True,\
                                            stop_words = False) for x in df['sent_tok'][i]] 

If you want to clean on the document level:

df['cleaned_data'] = df.text.apply(mts.clean_data, args=(True, True, False, True, False))

For the data cleaning function, we offer the following options:

• lower: make all the words to lowercase
• punctuations: remove all the punctuations in the corpus
• number: remove all the digits in the corpus
• unicode: remove all the unicodes in the corpus
• stop_words: remove the stopwords in the corpus

Boilerplate

df['Boilerplate'] = mts.Boilerplate(sent_tok, n = 4, min_doc = 5, get_ngram = False)

Parameters:

• input_data: this function requires tokenized documents.
• n: number of the ngrams to use. The default is 4.
• min_doc: when building the ngram list, ignore the ngrams that have a document frequency strictly lower than the given threshold. The default is 5 document. 30% of the number of the documents is recommended.
• get_ngram: if this parameter is set to "True" it will return a datafram with all the ngrams and the corresponding frequency, and "min_doc" parameter will become ineffective.
• max_doc: when building the ngram list, ignore the ngrams that have a document frequency strictly lower than the given threshold. The default is 75% of document. It can be percentage or integer.

Redundancy

df['Redundancy'] = mts.Redundancy(df.cleaned_data, n = 10)

Parameters:

• input_data: this function requires tokenized documents.
• n: number of the ngrams to use. The default is 10.

Specificity

df['Specificity'] = mts.Specificity(df.text)

Parameters:

• input_data: this function requires the documents without tokenization

Relative_prevalence

df['Relative_prevalence'] = mts.Relative_prevalence(df.text)

Parameters:

• input_data: this function requires the documents without tokenization

For the full code script, you may check here:

• Script
• Jupyter Notebook