| import streamlit as stl | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| from sklearn.pipeline import Pipeline | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.naive_bayes import MultinomialNB | |
| from sklearn.metrics import accuracy_score | |
| stl.write('this is sentiment analysis') | |
| df = pd.read_csv(r'combined_emotion.csv') | |
| model1 = Pipeline([("Feature_Engineer",CountVectorizer(binary= True,stop_words = 'english')), | |
| ("Algorithm", MultinomialNB())]) | |
| X = df['sentence'] | |
| y = df['emotion'] | |
| x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=23) | |
| model1.fit(x_train,y_train) | |
| y_pred = model1.predict(x_test) | |
| if stl.button('click for accuracy'): | |
| stl.write(accuracy_score(y_test,y_pred)) | |
| str = stl.text_input('enter text') | |
| if stl.button('click to check'): | |
| if model1.predict([str]) == 'sad': | |
| stl.write('sad ☹️') | |
| elif model1.predict([str]) == 'angry': | |
| stl.write('anger 😡') | |
| elif model1.predict([str]) == 'fear': | |
| stl.write('fear 😨') | |
| elif model1.predict([str]) == 'joy': | |
| stl.write('joy 😁') | |
| elif model1.predict([str]) == 'love': | |
| stl.write('😍') | |
| else : | |
| stl.write('surprise 😲') |