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pycoder2000pycoder2000
committedOct 26, 2021
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‎.gitattributes

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# Auto detect text files and perform LF normalization
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* text=auto

‎Procfile

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web: gunicorn app:app

‎README.md

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# Sentiment Analysis
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A simple sentiment analysis application made using following packages:
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- Tensorflow Keras
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- Natural Language Toolkit (NLTK)
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- TfidfVectorizer
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The model was trained using tweets from [Sentiment140 dataset with 1.6 million tweets](http://www.kaggle.com/kazanova/sentiment140).
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The app has has been deployed using Flask and Heroku. The app can be found [here](http://sentiment-analysis-mp.herokuapp.com/).

‎app.py

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import numpy as np
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from flask import Flask, request, jsonify, render_template
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import pickle
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import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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import tensorflow as tf
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import re
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from nltk.corpus import stopwords
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from nltk.stem import SnowballStemmer
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app = Flask(__name__)
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vect = pickle.load(open("vectorizer.pickle",'rb'))
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model = tf.keras.models.load_model('model.h5')
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def preprocess(text, stem=False):
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text = re.sub("@\S+|https?:\S+|http?:\S|[^A-Za-z0-9]+", ' ', str(text).lower()).strip()
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stop_words = stopwords.words('english')
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stemmer = SnowballStemmer('english')
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tokens = []
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for token in text.split():
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if token not in stop_words:
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if stem:
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tokens.append(stemmer.stem(token))
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else:
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tokens.append(token)
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return " ".join(tokens)
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@app.route('/')
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def home():
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return render_template('index.html')
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@app.route('/predict',methods=['POST'])
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def predict():
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inp = [x for x in request.form.values()]
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inp = preprocess(inp)
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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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if __name__ == "__main__":
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app.run(debug=True)

‎model.h5

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‎nltk.txt

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stopwords

‎requirements.txt

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Flask==1.1.2
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numpy==1.18.5
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nltk==3.5
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tensorflow==2.3.1
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scikit-learn==0.23.2
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gunicorn==20.0.4

‎sentiment-analysis.ipynb

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‎templates/index.html

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<!DOCTYPE html>
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<html >
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<head>
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<meta charset="UTF-8">
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<title>Sentiment Analysis</title>
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<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
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<link href="https://fonts.googleapis.com/css2?family=Roboto" rel='stylesheet' type='text/css'>
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<style type="text/css">
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body {
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background: #232526;
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background: -webkit-linear-gradient(to right, #414345, #232526);
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background: linear-gradient(to right, #414345, #232526);
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font-family: Roboto;
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}
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.main{
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margin-top: 150px;
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}
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.heading {
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color: #EFEFEF;
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font-size: 50px;
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}
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td {
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padding: 20px;
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}
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.senti-label {
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color: #fefefe;
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}
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input[type=text] {
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width: 35%;
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height: 40px;
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border-radius: 10px;
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padding: 5px 15px 5px 15px;
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border: 0px;
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}
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input[type=submit] {
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display: block;
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font-family: 'Open Sans', Helvetica, Arial, sans-serif;
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font-weight: 600;
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font-size: .75em;
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letter-spacing: 1px;
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height: 38px;
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width: 120px;
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line-height: 38px;
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overflow: hidden;
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background: #4dbecf;
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border-radius: 3px;
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box-shadow: 0 15px 30px rgba(black,.1);
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border: 0;
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cursor: pointer;
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transition: all .3s ease;
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}
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progress {
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width: 400px;
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height: 25px;
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background: white;
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border-radius: 45px;
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padding: 2px;
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box-shadow: 0 1px 0px 0 rgba(255, 255, 255, 0.2);
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}
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progress::-webkit-progress-bar {
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background: white;
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border-radius: 45px;
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padding: 2px;
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box-shadow: 0 1px 0px 0 rgba(255, 255, 255, 0.2);
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}
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progress::-webkit-progress-value{
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border-radius: 25px;
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box-shadow: inset 0 1px 1px 0 rgba(255, 255, 255, 0.4);
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background-size: 250px 25px;
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transition: width 2s ease-in-out;
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}
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.pos::-webkit-progress-value{
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background-color: green;
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}
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.neg::-webkit-progress-value{
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background-color: red;
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}
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</style>
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</head>
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<body>
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<div class="main" align="center">
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<h1 class="heading">Sentiment Analysis</h1>
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<form action="{{ url_for('predict')}}" method="post">
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<input type="text" name="input" placeholder="Enter text to analyse" required="required" /><br><br>
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<input type="submit" value="Analyse" id="sub">
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</form>
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<br>
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<br>
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<table>
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<tr>
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<td class="senti-label">Positive Sentiment</td>
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<td><progress min=0 max=100 value=100 id="posbar" class="pos"></progress></td>
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</tr>
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<tr>
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<td class="senti-label">Negative Sentiment</td>
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<td><progress min=0 max=100 value=100 id="negbar" class="neg"></progress></td>
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</tr>
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</table>
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</div>
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<script type="text/javascript">
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var val = {{prediction_text}};
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document.getElementById('posbar').value = val*100;
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document.getElementById('negbar').value = (1-val)*100;
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function dis(){
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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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</body>
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</html>

‎vectorizer.pickle

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