AutoNLPInsights extracts the Named Entities,Sentiments,Summary,KeyPhrases,Topics from the (Url/Plain Text/PDF Files ) and helps to visualize them (EDA) with one line of code.
git clone https://github.com/ncganesh/autonlpinsights
pip install -r requirements.txt
from autonlpinsights import nlpinsights
# Any URL or Plain Text
data = 'https://www.cnbc.com/2021/08/06/doximity-social-network-for-doctors-full-of-antivax-disinformation.html'
nlpinsight = nlpinsights(data)
nlpinsight.cleanedtext
# WORDCLOUDS
nlpinsight.visualize_wordclouds()
# NGRAMS
nlpinsight.visualize_ngrams(ngram_value = 2,top_n=5)
# NAMED ENTITY TREE MAP
nlpinsight.visualize_namedentities()
# For Vizualizing Raw text with Named Entities (Include spacy_fig = True)
nlpinsight.visualize_namedentities(spacy_fig = True)
# SENTIMENTS (Pie with sentiment labels )
nlpinsight.visualize_sentiments()
# SENTIMENT TABLE (Sentences sorted with sentiment score along with labels)
nlpinsight.get_sentiment_df()
# SUMMARY(Top 5 sentences using Abstarctive Summarization)
nlpinsight.get_summary_table()
# Gensim Topic Modelling
nlpinsight.visualize_topics(num_topics=3)
Note:
This is still in initial phase of Developement and will be adding more features soon