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app.py

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@@ -37,7 +37,7 @@ def predict():
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inp = vect.transform([inp]).toarray().reshape(1,1,2500)
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output = model.predict(inp)[0]
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return render_template('index.html', prediction_text='{}'.format(output[0]))
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return render_template('index.html', prediction_text='{}'.format(output[0]), anchor="services")
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if __name__ == "__main__":
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app.run(debug=True)

static/features-2.png

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static/features.png

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static/values-1.png

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templates/index.html

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@@ -164,14 +164,11 @@ <h2>Tech Stack</h2>
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<!-- ======= Services Section ======= -->
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<section id="services" class="services">
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<div class="container" data-aos="fade-up">
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<header class="section-header">
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<h2>Execution</h2>
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<p>Sentiment Analyzer</p>
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</header>
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<div class="form-group" align="center">
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<label for="exampleInputEmail1">Enter text to Analyze</label>
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<br>
@@ -505,6 +502,7 @@ <h6 style="color: #ff0071;">Classifications & Predictions</h6>
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<li class="list-group-item">The ML algorithm, trained by the training data, performs the analysis of the given data and provides the user with patterns and polarity.</li>
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<li class="list-group-item">Firstly, the program retrieves the data from twitter’s database, after searching the it from the keyword.</li>
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<li class="list-group-item">Then, the data is sent for preprocessing before sending it out for storage in warehouse.</li>
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<li class="list-group-item">The data is then aggregated as well as analyzed. Then, a final report as well as the data analysis is generated.</li>
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</ol>
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</div>
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<!-- End Tab 3 Content -->
@@ -824,7 +822,17 @@ <h4>Kushal Rajput</h4>
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document.getElementById("sub").value = 'Analysing...';
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document.getElementById("sub").disabled = true;
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}
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</script>
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</script>
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<script>
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function jumpToHash(hash){
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if (hash){
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var url = location.href;
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location.href = "#services";
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}
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}
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jumpToHash({{ anchor }});
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</script>
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<script src="{{ url_for('static',filename='bootstrap.bundle.js')}}"></script>
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<script src="{{ url_for('static',filename='aos.js')}}"></script>

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