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Sentiment.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[41]:
+
+
+import pandas as pd
+import nltk
+nltk.download('stopwords')
+nltk.download('punkt')
+import numpy as np
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+
+stop_words = set(stopwords.words("english"))
+
+
+# In[42]:
+
+
+data = pd.read_csv("sentimentdataset.csv")
+data.drop(columns = [i for i in data.columns if i not in ["Text","Sentiment"]], inplace = True)
+
+
+# In[ ]:
+
+
+def extract_words(sentence):
+    cleaned_text = [w.lower() for w in word_tokenize(sentence) if w.lower() not in stop_words]
+    return cleaned_text
+
+
+# In[ ]:
+
+
+def vocab(corpus):
+    vocabulary = []
+    
+    for doc in corpus:
+        words = extract_words(doc)
+        vocabulary.extend(words)
+        
+    vocabulary = sorted(list(set(vocabulary)))
+    
+    return vocabulary
+
+
+# In[ ]:
+
+
+def bow(sentence, vocabulary):
+    words = extract_words(sentence)
+    bag = np.zeros(len(vocabulary))
+    for word in words:
+        for i, vocab in enumerate(vocabulary):
+            if vocab == word:
+                bag[i] += 1
+    return bag
+
+
+# In[ ]:
+
+
+vocabulary = vocab(data.Text.to_list())
+
+
+# In[ ]:
+
+
+arrays = np.empty((0, len(vocabulary)), int)
+for val in data.Text.to_list():
+
+    bow_representation = bow(val, vocabulary)
+    arrays = np.append(arrays, [bow_representation], axis=0)
+    
+
+
+# In[ ]:
+
+
+from sklearn.preprocessing import LabelEncoder
+label_encoder = LabelEncoder()
+data['Encoded_Sentiment'] = label_encoder.fit_transform(data['Sentiment'])
+
+
+# In[ ]:
+
+
+print("Mapping of original labels to encoded labels:")
+for original_label, encoded_label in zip(label_encoder.classes_, label_encoder.transform(label_encoder.classes_)):
+    print(f"{original_label}: {encoded_label}")
+
+
+# In[ ]:
+
+
+X = arrays
+y = data['Encoded_Sentiment']
+
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Initialize the Random Forest model
+rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)
+
+# Train the model on the training set
+rf_classifier.fit(X, y)
+
+Labels = dict(zip(label_encoder.transform(label_encoder.classes_),label_encoder.classes_))
+
+def pred(text):
+    bag = bow(text, vocabulary)
+    input_array = np.array([bag])
+    y_pred = rf_classifier.predict(input_array)
+    return y_pred
+
+# inputt = input("Enter the Text input: ")
+# predicted_label = pred(inputt)[0]
+# print("Predicted Label:", Labels[predicted_label])
+
+
+# In[ ]:
+
+
+import streamlit as st
+
+
+# In[43]:
+emojis = {
+    ' Acceptance   ': '😊',
+    ' Acceptance      ': '😊',
+    ' Accomplishment ': '🏆',
+    ' Admiration ': '😍',
+    ' Admiration   ': '😍',
+    ' Admiration    ': '😍',
+    ' Adoration    ': '😍',
+    ' Adrenaline     ': '🤯',
+    ' Adventure ': '🚀',
+    ' Affection    ': '❤️',
+    ' Amazement ': '😲',
+    ' Ambivalence ': '😐',
+    ' Ambivalence     ': '😐',
+    ' Amusement    ': '😄',
+    ' Amusement     ': '😄',
+    ' Anger        ': '😡',
+    ' Anticipation ': '😬',
+    ' Anticipation  ': '😬',
+    ' Anxiety   ': '😰',
+    ' Anxiety         ': '😰',
+    ' Appreciation  ': '🙏',
+    ' Apprehensive ': '😟',
+    ' Arousal       ': '😏',
+    ' ArtisticBurst ': '🎨',
+    ' Awe ': '😲',
+    ' Awe    ': '😲',
+    ' Awe          ': '😲',
+    ' Awe           ': '😲',
+    ' Bad ': '😞',
+    ' Betrayal ': '😔',
+    ' Betrayal      ': '😔',
+    ' Bitter       ': '😖',
+    ' Bitterness ': '😖',
+    ' Bittersweet ': '😔😊',
+    ' Blessed       ': '🙏',
+    ' Boredom ': '😑',
+    ' Boredom         ': '😑',
+    ' Breakthrough ': '🎉',
+    ' Calmness     ': '😌',
+    ' Calmness      ': '😌',
+    ' Captivation ': '😍',
+    ' Celebration ': '🎉',
+    ' Celestial Wonder ': '🌟',
+    ' Challenge ': '💪',
+    ' Charm ': '😊',
+    ' Colorful ': '🎨',
+    ' Compassion': '❤️',
+    ' Compassion    ': '❤️',
+    ' Compassionate ': '❤️',
+    ' Confidence    ': '😎',
+    ' Confident ': '😎',
+    ' Confusion ': '😕',
+    ' Confusion    ': '😕',
+    ' Confusion       ': '😕',
+    ' Connection ': '💑',
+    ' Contemplation ': '🤔',
+    ' Contentment ': '😊',
+    ' Contentment   ': '😊',
+    ' Coziness     ': '🏠',
+    ' Creative Inspiration ': '🎨',
+    ' Creativity ': '🎨',
+    ' Creativity   ': '🎨',
+    ' Culinary Adventure ': '🍽️',
+    ' CulinaryOdyssey ': '🍽️',
+    ' Curiosity ': '🤔',
+    ' Curiosity  ': '🤔',
+    ' Curiosity   ': '🤔',
+    ' Curiosity     ': '🤔',
+    ' Curiosity       ': '🤔',
+    ' Darkness     ': '🌑',
+    ' Dazzle        ': '✨',
+    ' Desolation ': '😞',
+    ' Despair ': '😢',
+    ' Despair   ': '😢',
+    ' Despair      ': '😢',
+    ' Despair         ': '😢',
+    ' Desperation ': '😟',
+    ' Determination ': '💪',
+    ' Determination   ': '💪',
+    ' Devastated ': '😭',
+    ' Disappointed ': '😞',
+    ' Disappointment ': '😞',
+    ' Disgust ': '🤢',
+    ' Disgust      ': '🤢',
+    ' Disgust         ': '🤢',
+    ' Dismissive ': '😒',
+    ' DreamChaser   ': '🚀',
+    ' Ecstasy ': '😆',
+    ' Elation   ': '😆',
+    ' Elation       ': '😆',
+    ' Elegance ': '💃',
+    ' Embarrassed ': '😳',
+    ' Emotion ': '😄',
+    ' EmotionalStorm ': '😱',
+    ' Empathetic ': '❤️',
+    ' Empowerment   ': '💪',
+    ' Enchantment ': '😍',
+    ' Enchantment   ': '😍',
+    ' Energy ': '⚡',
+    ' Engagement ': '💍',
+    ' Enjoyment    ': '😊',
+    ' Enthusiasm ': '😃',
+    ' Enthusiasm    ': '😃',
+    ' Envious ': '😠',
+    ' Envisioning History ': '🏰',
+    ' Envy            ': '😠',
+    ' Euphoria ': '😁',
+    ' Euphoria   ': '😁',
+    ' Euphoria     ': '😁',
+    ' Euphoria      ': '😁',
+    ' Excitement ': '😃',
+    ' Excitement   ': '😃',
+    ' Excitement    ': '😃',
+    ' Exhaustion ': '😩',
+    ' Exploration ': '🌍',
+    ' Fear         ': '😨',
+    ' Fearful ': '😨',
+    ' FestiveJoy    ': '🎉',
+    ' Free-spirited ': '🦋',
+    ' Freedom       ': '🗽',
+    ' Friendship ': '👫',
+    ' Frustrated ': '😤',
+    ' Frustration ': '😤',
+    ' Frustration     ': '😤',
+    ' Fulfillment  ': '😊',
+    ' Fulfillment   ': '😊'}
+
+def main():
+    st.title("Text Sentiment Classifier")
+    selected_text = st.selectbox("Select Text", data['Text'].tolist())
+    if st.button("Predict"):
+        predicted_label = pred(selected_text)[0]
+        try:
+            st.write("Predicted Sentiment:", Labels[predicted_label], emojis[Labels[predicted_label]])
+        except:
+            st.write("Predicted Sentiment:", Labels[predicted_label])
+        
+
+if __name__ == "__main__":
+    main()
+
+
+# In[ ]:
+
+
+
+
+

requirements.txt

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+nltk==3.6.3
+numpy==1.21.2
+pandas==1.3.3
+scikit-learn==0.24.2
+streamlit==1.3.0
+