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import nltk | |
from nltk.sentiment import SentimentIntensityAnalyzer | |
# Download the vader_lexicon resource | |
nltk.download('vader_lexicon') | |
text = "I absolutely loved this movie! The acting was superb, the plot was engaging, and the cinematography was stunning. I would highly recommend it to anyone looking for a great film to watch.." | |
analyzer = SentimentIntensityAnalyzer() | |
scores = analyzer.polarity_scores(text) | |
if scores['compound'] >= 0.05: | |
print("Positive Sentiment") | |
elif scores['compound'] <= -0.05: | |
print("Negative Sentiment") | |
else: | |
print("Neutral Sentiment") | |
import gradio as gr | |
import nltk | |
from nltk.sentiment import SentimentIntensityAnalyzer | |
nltk.download('vader_lexicon') | |
analyzer = SentimentIntensityAnalyzer() | |
def analyze_sentiment(text): | |
scores = analyzer.polarity_scores(text) | |
if scores['compound'] >= 0.5: | |
sentiment = "Very Positive π" | |
elif scores['compound'] > 0 and scores['compound'] < 0.5: | |
sentiment = "Positive π" | |
elif scores['compound'] == 0: | |
sentiment = "Neutral π" | |
elif scores['compound'] > -0.5 and scores['compound'] < 0: | |
sentiment = "Negative π" | |
elif scores['compound'] <= -0.5: | |
sentiment = "Very Negative π " | |
elif "racist" in text.lower(): | |
sentiment = "Racist π€¬" | |
elif "annoying" in text.lower(): | |
sentiment = "Annoying π" | |
elif "boring" in text.lower(): | |
sentiment = "Boring π΄" | |
else: | |
sentiment = "Unknown π" | |
return sentiment, text | |
iface = gr.Interface(fn=analyze_sentiment, | |
inputs=gr.inputs.Textbox(label="Enter Text Here"), | |
outputs=[gr.outputs.Textbox(label="Sentiment"), | |
gr.outputs.Textbox(label="Input Text")], | |
title="Sentiment Analysis", | |
description="Enter a sentence and get the sentiment analysis result.") | |
iface.launch() | |