EmoTFIDF 1.2.8

A library to extract emotions using two methods, 1- Using lexicon based, counting frequency of emotion2- Integrating TFIDF to add a contextNote that lexicon license is for research purposes only.

EmoTFIDF is an emotion detection library (Lexicon approach) based in the National Research Council Canada (NRC) and this package is for research purposes only. Source: [lexicons for research] (http://sentiment.nrc.ca/lexicons-for-research/)

#This library provides two types of emotions:

1- Lexicon based emotions which counting the frequency of the emotion based on the lexicon 2- Integrating TFIDF to add a context to the emotions.

#Installation

pip install EmoTFIDF

#List of emotions

-fear -anger -anticipation -trust -surprise -positive -negative -sadness -disgust -joy

#Example of usage ##Get emotions from a sentence

from EmoTFIDF import EmoTFIDF

comment = "I had a GREAT week, thanks to YOU! I am very happy today."

emTFIDF = EmoTFIDF()

emTFIDF.set_text(comment)
print(emTFIDF.em_frequencies)

##Get emotions factorising TFIDF, you will need to add a context

Below is an example in pandas assuming you have a list of tweets/text and you would want to get emotions

emTFIDF  = EmoTFIDF()
def getEmotionsTFIDF(s,emTFIDF):
  emTFIDF.set_text(s)
  emTFIDF.get_emotfidf()
  return emTFIDF.em_tfidf


emTFIDF.computeTFIDF(df['text'])
df['emotions'] = new_df.apply(lambda x: getEmotionsTFIDF(x['text'], emTFIDF), axis=1)#em_tfidf
df2 = df['emotions'].apply(pd.Series)
final_df = pd.concat([df,df2],axis=1)

#Plotting Emotion Distribution You can visualize the distribution of emotions using the plot_emotion_distribution method:

from EmoTFIDF import EmoTFIDF

comment = "I had a GREAT week, thanks to YOU! I am very happy today."

emTFIDF = EmoTFIDF()
emTFIDF.set_text(comment)
emTFIDF.plot_emotion_distribution()

#Plotting Top TFIDF Words To visualize the top N words by their TFIDF scores:

import pandas as pd
from EmoTFIDF import EmoTFIDF

# Assuming df is your DataFrame and it has a column 'text'
emTFIDF = EmoTFIDF()
emTFIDF.compute_tfidf(df['text'])
emTFIDF.plot_top_tfidf(top_n=20)

#Plotting TFIDF Weighted Emotion Scores To visualize the TFIDF weighted emotion scores:

from EmoTFIDF import EmoTFIDF

comment = "I had a GREAT week, thanks to YOU! I am very happy today."

emTFIDF = EmoTFIDF()
emTFIDF.set_text(comment)
emTFIDF.get_emotfidf()
emTFIDF.plot_emotfidf()

##Update 1.3.0

Introduced new plotting features to visualize the distribution of emotions, top TFIDF words, and TFIDF weighted emotion scores.

New Methods: plot_emotion_distribution(): Visualizes the distribution of emotions in the text. plot_top_tfidf(top_n=20): Visualizes the top N words by their TFIDF scores. plot_emotfidf(): Visualizes the TFIDF weighted emotion scores. These features enhance the interpretability of the emotion analysis by providing insightful visualizations.

##Update 1.0.7

Thanks to artofchores, from Reddit for his feedback.

Added a set_lexicon_path option if you would like to use your own lexicon Remember to keep the same structure as the original emotions lexicon which located here

emTFIDF.set_lexicon_path("other_lexicon.json")

##Update 1.1.1

Updated the lexical db with some help from ChatGPT