docs2tops 0.0.3

Takes a list of documents and returns fully automated & labeled dictionaries where topic names are keys and semantically similar keywords from the documents as values

docs2tops stands for documents to topics.

What it basically does is:

• extracting ngrams from the documents
• extracting meaningful moregrams (2 or more grams)
• creates semi-automated dictionary - if user provided some possible topics, docs2tops provides similar keywords per topics provided
• creates fully-automated dictionary

in both cases (either user inputs some topics or not), docs2tops returns 2 dictionaries. if user did not provide any topic, first dictionary will be empty with a message only.

in all cases, fully-automated dictionary will be created.

docs2tops function takes list of documents optionally, you can provide candidate_topics_list, moregrams_sample_size.

docs2tops(docs_input_list, candidate_topics_list=None, moregrams_sample_size=None)

installation

Run the following to install:

pip install docs2tops

usage

from docs2tops import docs2tops
import pandas as pd

df = pd.read_csv(r"C:\Users\my_file.csv")
docs = df['my_texual_content'].to_list()

candidate_topics_list = ['smell', 'taste', 'delivery', 'packaging']
moregrams_sample_size = 100


user_input_dict, fully_auto_dict = docs2tops(docs_input_list=docs,
              candidate_topics_list=candidate_topics_list, 
              moregrams_sample_size=moregrams_sample_size)

list_dicts = [user_input_dict, fully_auto_dict]
for result in list_dicts:
    print(result)
    print('number of topics: ', len(result))
    print('---')

Developing docs2tops

to install docs2tops, along with the tools you need to develop and run tests, run the following in your virtual environment:

pip install -e .[dev]