ProfileNER-classifier 0.0.2

Performs Classification using Regex for social media user's profile categorization and NER tasks

NLP Classification Python Package For Profile Category and NER

Functionality of the Package

Performs Classification using Regex for social media user's profile categorization and NER tasks

1. If profile:
   • Categories: Politician, Information Vehicule, Health Professional, Science, Education Professional, Artist, Organization and Journalist;
2. NER:
   • Vaccines, Products, Drugs, Diseases, Symptoms, Science, Part of the body; returns a dataframe that has information of the entity id, name and it's occurence frequency on the input text.

The package takes the following parameters as input:

1. Profile:
   • Text containing informations about the user's biography or channel description in the context of communication reaseach porpuses
2. NER:
   • Text

Usage

pip install ProfileNER-classifier

Example

from nlpclassifier_profilener import NLPClassifier

#instatiate the classifier
tpc = TwitterProfileClassifier('profile')
#preprocess the text. Its's important to lowercase and remove accents
yt['channelDesc'] = yt['channelDesc'].apply(lambda x: pipeline.preprocess(x, lower = True)).apply(lambda x: pipeline.strip_accents(x))
#classify channelCategory
yt['channelCategory'] = yt['channelDesc'].apply(lambda x: tpc.classifier(str(x)))

yt['channelCategory'].value_counts()

Note

The package is currently in a very elementary stage and work is in progress. In case of error, feel free to contact me.

Change Log

0.0.2 (03/09/2022)

• First Release