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Create app.py
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app.py
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import nltk
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from nltk.sentiment import SentimentIntensityAnalyzer
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# Download the vader_lexicon resource
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nltk.download('vader_lexicon')
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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.."
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analyzer = SentimentIntensityAnalyzer()
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scores = analyzer.polarity_scores(text)
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if scores['compound'] >= 0.05:
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print("Positive Sentiment")
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elif scores['compound'] <= -0.05:
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print("Negative Sentiment")
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else:
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print("Neutral Sentiment")
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import gradio as gr
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import nltk
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from nltk.sentiment import SentimentIntensityAnalyzer
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nltk.download('vader_lexicon')
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analyzer = SentimentIntensityAnalyzer()
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def analyze_sentiment(text):
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scores = analyzer.polarity_scores(text)
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if scores['compound'] >= 0.5:
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sentiment = "Very Positive π"
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elif scores['compound'] > 0 and scores['compound'] < 0.5:
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sentiment = "Positive π"
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elif scores['compound'] == 0:
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sentiment = "Neutral π"
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elif scores['compound'] > -0.5 and scores['compound'] < 0:
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sentiment = "Negative π"
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elif scores['compound'] <= -0.5:
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sentiment = "Very Negative π "
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elif "racist" in text.lower():
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sentiment = "Racist π€¬"
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elif "annoying" in text.lower():
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sentiment = "Annoying π"
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elif "boring" in text.lower():
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sentiment = "Boring π΄"
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else:
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sentiment = "Unknown π"
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return sentiment, text
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iface = gr.Interface(fn=analyze_sentiment,
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inputs=gr.inputs.Textbox(label="Enter Text Here"),
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outputs=[gr.outputs.Textbox(label="Sentiment"),
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gr.outputs.Textbox(label="Input Text")],
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title="Sentiment Analysis",
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description="Enter a sentence and get the sentiment analysis result.")
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iface.launch()
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