Merge branch 'larymak:main' into main

Author: prathu10

MachineLearning Projects/Student-Feedback-Sentiment-Analysis-main/Student-Feedback-Sentiment-Analysis-main/Procfile

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+web: gunicorn app:app

MachineLearning Projects/Student-Feedback-Sentiment-Analysis-main/Student-Feedback-Sentiment-Analysis-main/README.md

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+
+## PROJECT OUTLINE
+The goal of this project is to develop an automated robust feedback analysis system wherein the user has to first upload form response data in csv format, then we process and extract the data with the help of NLTK and then applying sentiment analysis using Naive Bayes algorithm to extract the opinions and subjectivity from the student feedback by classifying them into 3 different categories: 
+- positive 
+- neutral 
+- negative
+
+Nowadays, Educational Institutions are giving a lot of emphasis on maintaining Student Feedback Systems so as to understand the patterns generated by student feedback data to effectively improve the performance of the institution. There is also a requirement to automate the student feedback system in order to handle a large amount of data and analyse them effectively.
+
+Our feedback analysis model will thus enable the concerned authorities to understand the student’s  perception and their expected level of learning materials and course content. Analysing student feedback would help institutions to enhance student’s learning capabilities in the classroom and improve the overall quality of education.
+
+## WEBSITE LINK
+_https://get-analysis.herokuapp.com/_
+
+## DEMO OF WORKING MODEL 
+_[Project Demo](https://drive.google.com/file/d/1hAu_Rmdu8qq6yjq8DSgD-P08EA4nTuVu/view?usp=sharing)_ 
+
+<br>
+
+## PROJECT WORKFLOW
+![outline](images/flow.PNG)
+
+<br>
+
+## RESULTS
+![results](images/results.PNG)
+
+<br>
+
+## WEBSITE LAYOUT
+![results](images/1.png)
+
+![results](images/2.png)
+
+![results](images/3.png)
+

MachineLearning Projects/Student-Feedback-Sentiment-Analysis-main/Student-Feedback-Sentiment-Analysis-main/app.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import json
+import faculty
+import drawFigure
+
+
+from flask import Flask , render_template , redirect , request
+
+app = Flask(__name__)
+
+
+with open('Student-Feedback-Sentiment-Analysis-main/feedback1.json') as file:
+    json_string = file.read()
+    documents1 = json.loads(json_string)
+    
+with open('Student-Feedback-Sentiment-Analysis-main/feedback2.json') as file:
+    json_string = file.read()
+    documents2 = json.loads(json_string)
+    
+with open('Student-Feedback-Sentiment-Analysis-main/feedback3.json') as file:
+    json_string = file.read()
+    documents3 = json.loads(json_string)
+    
+with open('Student-Feedback-Sentiment-Analysis-main/feedback4.json') as file:
+    json_string = file.read()
+    documents4 = json.loads(json_string)
+    
+with open('Student-Feedback-Sentiment-Analysis-main/feedback5.json') as file:
+    json_string = file.read()
+    documents5 = json.loads(json_string)
+    
+with open('Student-Feedback-Sentiment-Analysis-main/feedback6.json') as file:
+    json_string = file.read()
+    documents6 = json.loads(json_string)
+
+label2category = {1: 'positive' , 0: 'neutral' , -1: 'negative'}
+category2label = {cat:label for label , cat in label2category.items()}
+
+
+categories1 = [category2label[category] for doc , category in documents1]
+categories2 = [category2label[category] for doc , category in documents2]
+categories3 = [category2label[category] for doc , category in documents3]
+categories4 = [category2label[category] for doc , category in documents4]
+categories5 = [category2label[category] for doc , category in documents5]
+categories6 = [category2label[category] for doc , category in documents6]
+
+
+corpus1 = [' '.join(document) for document , cat in documents1]
+corpus2 = [' '.join(document) for document , cat in documents2]
+corpus3 = [' '.join(document) for document , cat in documents3]
+corpus4 = [' '.join(document) for document , cat in documents4]
+corpus5 = [' '.join(document) for document , cat in documents5]
+corpus6 = [' '.join(document) for document , cat in documents6]
+
+
+
+@app.route('/')
+def display():
+    return render_template('index.html')
+
+
+@app.route('/' , methods=['POST'])
+def caption():
+
+    if request.method == 'POST':
+
+        f = request.files["file_name"]
+        path = "Student-Feedback-Sentiment-Analysis-main/static/Student-Feedback-Form.csv"     
+        f.save(path)
+
+        category_no = int(request.form['Cate'])
+
+        df = pd.read_csv(path)
+
+        cols1 = []
+        cols2 = []
+        cols3 = []
+        cols4 = []
+        cols5 = []
+        cols6 = []
+
+        substring1 = ['teacher' , 'faculty' , 'feedback' , 'effectiveness' , 'teaching' , 'knowledge' , 'delivery' , 'content' , 'quality' , 
+                    'lecture' , 'subject' , 'syllabus' , 'review' , 'assessment']
+        substring2 = ['course' , 'content' , 'syllabus' , 'review' , 'evaluation' , 'curriculum' , 'syllabi' , 'contents' , 'level' , 
+                    'difficulty' , 'lecture' , 'outline']
+        substring3 = ['exam' , 'examination' , 'pattern' , 'conduct' , 'question' , 'paper' , 'level' , 'outline']
+        substring4 = ['laboratory' , 'laboratories' , 'lab' , 'facility' , 'facilities' , 'review' , 'feedback' , 'rate' , 'learning' ]
+        substring5 = ['library' , 'facilities' , 'books' , 'availability' , 'facility' , 'material' , 'rate' , 'feedback' , 'review']
+        substring6 = ['extra' , 'curricular' , 'activity' , 'activities']
+
+
+        for i in list(df.columns):
+            for j in substring1:
+                if j.casefold() in i.casefold():
+                    cols1.append(df.columns.get_loc(i))
+                    if cols1 != []:
+                        break
+                        
+        for i in list(df.columns):
+            for j in substring2:
+                if j.casefold() in i.casefold():
+                    cols2.append(df.columns.get_loc(i))
+                    if cols2 != []:
+                        break    
+
+        for i in list(df.columns):
+            for j in substring3:
+                if j.casefold() in i.casefold():
+                    cols3.append(df.columns.get_loc(i))
+                    if cols3 != []:
+                        break
+                        
+        for i in list(df.columns):
+            for j in substring4:
+                if j.casefold() in i.casefold():
+                    cols4.append(df.columns.get_loc(i))
+                    if cols4 != []:
+                        break
+                        
+                        
+        for i in list(df.columns):
+            for j in substring5:
+                if j.casefold() in i.casefold():
+                    cols5.append(df.columns.get_loc(i))
+                    if cols5 != []:
+                        break
+                        
+        for i in list(df.columns):
+            for j in substring6:
+                if j.casefold() in i.casefold():
+                    cols6.append(df.columns.get_loc(i))
+                    if cols6 != []:
+                        break
+                        
+        cols = cols1+cols2+cols3+cols4+cols5+cols6
+        cols = list(set(cols))
+        
+        df_form = pd.read_csv(path , usecols = cols)
+        reviews = np.array(df_form)
+        
+        pos1 , n1 , neg1 = faculty.predict(corpus1 , categories1 , reviews[: , 0])
+        pos2 , n2 , neg2 = faculty.predict(corpus1 , categories1 , reviews[: , 1])
+        pos3 , n3 , neg3 = faculty.predict(corpus1 , categories1 , reviews[: , 2])
+        pos4 , n4 , neg4 = faculty.predict(corpus1 , categories1 , reviews[: , 3])
+        pos5 , n5 , neg5 = faculty.predict(corpus1 , categories1 , reviews[: , 4])
+        pos6 , n6 , neg6 = faculty.predict(corpus1 , categories1 , reviews[: , 5])
+
+        results = {
+            'f1' : 'Teacher Feedback',
+            'pos1' : pos1,
+            'n1' : n1,
+            'neg1' : neg1,
+            'f2' : 'Course Content',
+            'pos2' : pos2,
+            'n2' : n2,
+            'neg1' : neg2,
+            'f3' : 'Examination pattern',
+            'pos3' : pos3,
+            'n3' : n3,
+            'neg3' : neg3,
+            'f4' : 'Laboratory',
+            'pos4' : pos4,
+            'n4' : n4,
+            'neg4' : neg4,
+            'f5' : 'Library Facilities',
+            'pos5' : pos5,
+            'n5' : n5,
+            'neg5' : neg5,
+            'f6' : 'Extra Co-Curricular Activities',
+            'pos6' : pos6,
+            'n6' : n6,
+            'neg6' : neg6,
+        }
+
+        values = list([[pos1 , n1 , neg1], [pos2 , n2 , neg2], [pos3 , n3 , neg3], [pos4 , n4 , neg4], [pos5 , n5 , neg5], [pos6 , n6 , neg6]])
+        labels = list(['Teacher Feedback', 'Course Content', 'Examination pattern','Laboratory','Library Facilities', 'Extra Co-Curricular Activities'])
+        
+        print(values[category_no-1] , labels[category_no-1] , category_no , category_no-1)
+        
+        if category_no == 1:
+            results_1 = {
+                'f1' : 'Teacher Feedback',
+                'pos1' : pos1,
+                'n1' : n1,
+                'neg1' : neg1
+            }
+
+            drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
+
+            return render_template('index1.html' , result1 = results_1 , cat = category_no) 
+
+
+        elif category_no == 2:
+            results_2 = {
+                'f2' : 'Course Content',
+                'pos2' : pos2,
+                'n2' : n2,
+                'neg2' : neg2
+            }
+
+            drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
+
+            return render_template('index1.html' , result2 = results_2 , cat = category_no)
+
+
+        elif category_no == 3:
+            results_3 = {
+                'f3' : 'Examination pattern',
+                'pos3' : pos3,
+                'n3' : n3,
+                'neg3' : neg3
+            }
+
+            drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
+
+            return render_template('index1.html' , result3 = results_3 , cat = category_no)
+
+
+        elif category_no == 4:
+            results_4 = {
+                'f4' : 'Laboratory',
+                'pos4' : pos4,
+                'n4' : n4,
+                'neg4' : neg4
+            }
+
+            drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
+
+            return render_template('index1.html' , result4 = results_4 , cat = category_no)
+        
+
+        elif category_no == 5:
+            results_5 = {
+                'f5' : 'Library Facilities',
+                'pos5' : pos5,
+                'n5' : n5,
+                'neg5' : neg5
+            }
+
+            drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)       
+
+            return render_template('index1.html' , result5 = results_5 , cat = category_no)
+
+
+        elif category_no == 6:
+            results_6 = {
+                'f6' : 'Extra Co-Curricular Activities',
+                'pos6' : pos6,
+                'n6' : n6,
+                'neg6' : neg6
+            }
+
+            drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)           
+
+            return render_template('index1.html' , result6 = results_6 , cat = category_no)
+
+
+        else:            
+            for i in range(0 , 6):
+                fig = plt.figure(figsize=(8,8) , edgecolor='red' , linewidth=10)
+                plt.bar(x = ['Positive' , 'Neutral' , 'Negative'] , height = values[i] , color=['blue','gold','red'])
+                plt.title(labels[i], fontsize = 24, weight = 'demibold', pad = 15, fontstyle = 'italic' , family = 'cursive')
+                plt.xticks(rotation=0 , fontsize=16)
+                plt.yticks([])
+                plt.xlabel('Feedback Type',fontsize = 18, labelpad=17, weight= 550 , family = 'cursive')
+                plt.ylabel('')
+                fig.subplots_adjust(bottom = 0.14)
+                ax = plt.gca()
+                ax.spines['right'].set_visible(False)
+                ax.spines['top'].set_visible(False)
+                ax.spines['left'].set_visible(False)
+                for p in ax.patches:
+                    ax.annotate("%.1f%%" % (100*float(p.get_height()/sum(values[i]))), (p.get_x() + p.get_width() / 2., abs(p.get_height())),
+                    ha='center', va='bottom', color='black', xytext=(0, 5),rotation = 'horizontal',
+                    textcoords='offset points', fontsize = 16 , fontweight = 'medium')
+                plt.savefig(f'Student-Feedback-Sentiment-Analysis-main/static/plot{i+10}.jpg')
+
+            return render_template('index1.html' , result = results)  
+    
+    else:
+        return render_template('error.html')
+
+
+if __name__ == '__main__':
+    app.run(debug=True)

MachineLearning Projects/Student-Feedback-Sentiment-Analysis-main/Student-Feedback-Sentiment-Analysis-main/drawFigure.py

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+import matplotlib.pyplot as plt
+import numpy as np
+
+
+
+def make(value , label , category_no):
+
+    fig = plt.figure(figsize=(8,8) , edgecolor='red' , linewidth=10)
+
+    plt.bar(x = ['Positive' , 'Neutral' , 'Negative'] , height = value , color=['blue','gold','red'])
+    plt.title(label, fontsize = 24, weight = 'demibold', pad = 15, fontstyle = 'italic' , family = 'cursive')
+    plt.xticks(rotation=0 , fontsize=16)
+    plt.yticks([])
+    plt.xlabel('Feedback Type',fontsize = 18, labelpad=17, weight= 550 , family = 'cursive')
+    plt.ylabel('')
+
+    fig.subplots_adjust(bottom = 0.14)
+    ax = plt.gca()
+    ax.spines['right'].set_visible(False)
+    ax.spines['top'].set_visible(False)
+    ax.spines['left'].set_visible(False)
+
+    for p in ax.patches:
+        ax.annotate("%.1f%%" % (100*float(p.get_height()/sum(value))), (p.get_x() + p.get_width() / 2., abs(p.get_height())),
+        ha='center', va='bottom', color='black', xytext=(0, 5),rotation = 'horizontal',
+        textcoords='offset points', fontsize = 16 , fontweight = 'medium')
+        
+    plt.savefig(f'./static/plot{category_no}.jpg')
+
+    return

MachineLearning Projects/Student-Feedback-Sentiment-Analysis-main/Student-Feedback-Sentiment-Analysis-main/faculty.py

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+
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.feature_extraction.text import CountVectorizer
+
+label2category = {1: 'positive' , 0: 'neutral' , -1: 'negative'}
+category2label = {cat:label for label , cat in label2category.items()}
+
+def predict(corpus , categories , text_array): 
+
+    cv = CountVectorizer(max_features = 1000 , ngram_range = (1,2) , max_df = 0.9)
+    mnb = MultinomialNB()
+    mnb.fit(cv.fit_transform(corpus).toarray() , categories)
+    
+    preds = [label2category[label] for label in mnb.predict(cv.transform(text_array).toarray())]
+    
+    analysis = {'positive':0 , 'neutral':0 , 'negative':0}
+    for label in preds:
+        analysis[label] += 1
+   
+    return analysis['positive'] , analysis['neutral'] , analysis['negative']
+
+
+