Metadata-Version: 2.1
Name: textslack
Version: 0.1.5
Summary: Play with text data
Home-page: https://github.com/AnkitRajSri/textslack.git
Author: Ankit Raj
Author-email: srivastavaankit667@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: nltk
Requires-Dist: textblob
Requires-Dist: sklearn
Requires-Dist: normalise

# textslack
A text cleaning pipeline to perform text cleaning, along with additional functionalities for sentiment, pos extraction, and word count.

After pip install, please follow the below step to access the functionalities:
* from textslack.textslack import TextSlack
* slack = TextSlack()

Below are the key functionalities currently available in the all the versions:

1. transform(text): normalises and cleans unstructured text
2. extract_nouns(text): cleans text to keep only nouns
3. extract_verbs(text): cleans text to keep only verbs
4. extract_adjectives(text): cleans text to keep only adjectives
5. extract_adverbs(text): cleans text to keep only adverbs
6. sentiment(text): returns a string as sentiment
7. word_occurances(word, text): returns frequency of a word mentioned in the text

v.0.1.5 updates: 
1. transform method can now be used on a list and an entire pandas dataframe column.
2. textslack can perform basic text cleaning for some non-english languages as well, just pass the language while creating the object as shown below.

   slack = TextSlack(lang='spanish')

Please refer the below medium article for a detailed explanation of textslack functionalities.
https://medium.com/analytics-vidhya/text-processing-made-easy-with-textslack-4214ae6bc67a


