Engineeering & IT Projects and Resources: EdTech Feedback Sentiment Analysis

EdTech Feedback Sentiment Analysis

1. Introduction

Objective: Perform sentiment analysis on student feedback to classify reviews into positive, negative, or neutral categories.
Purpose: Help EdTech platforms improve their services by understanding user sentiments and addressing key concerns.

2. Project Workflow

1. Problem Definition:
   - Classify student feedback into sentiment categories.
   - Key questions:
     - What are the key sentiments expressed in the reviews?
     - Which aspects of the service need improvement based on feedback?
2. Data Collection:
   - Source: Feedback forms, surveys, or scraped reviews from EdTech platforms.
   - Fields: Review Text, Date, Rating (if available).
3. Data Preprocessing:
   - Text cleaning, tokenization, and normalization.
4. Sentiment Classification:
   - Use Natural Language Processing (NLP) to classify feedback into sentiment categories.
5. Visualization:
   - Display sentiment trends and key themes.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- NLP: NLTK, spaCy, TextBlob, Transformers (Hugging Face)
- Visualization: Matplotlib, Seaborn, WordCloud

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy nltk spacy textblob matplotlib seaborn wordcloud transformers
```
Download spaCy model:
```
python -m spacy download en_core_web_sm
```

Step 2: Load and Explore Dataset

Load the dataset containing student reviews:
```
import pandas as pd

data = pd.read_csv("student_feedback.csv")
print(data.head())
```
Explore the distribution of reviews:
```
print(data['Review'].describe())
```

Step 3: Preprocess Data

Clean and preprocess the text data:
```
import re
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords

stop_words = set(stopwords.words('english'))

def preprocess_text(text):
    text = re.sub(r'[^a-zA-Z]', ' ', text.lower())
    tokens = word_tokenize(text)
    tokens = [word for word in tokens if word not in stop_words]
    return ' '.join(tokens)

data['cleaned_review'] = data['Review'].apply(preprocess_text)
```

Step 4: Sentiment Analysis

1. Using TextBlob for Basic Sentiment Analysis:
```
from textblob import TextBlob

def get_sentiment(text):
    analysis = TextBlob(text)
    if analysis.sentiment.polarity > 0:
        return 'Positive'
    elif analysis.sentiment.polarity < 0:
        return 'Negative'
    else:
        return 'Neutral'

data['sentiment'] = data['cleaned_review'].apply(get_sentiment)
```
2. Using Pre-trained Transformer Models for Advanced Analysis:
```
from transformers import pipeline

classifier = pipeline('sentiment-analysis')
data['sentiment'] = data['cleaned_review'].apply(lambda x: classifier(x)[0]['label'])
```

Step 5: Visualization

1. Sentiment Distribution:
```
import seaborn as sns
import matplotlib.pyplot as plt

sns.countplot(data['sentiment'])
plt.title("Sentiment Distribution")
plt.show()
```
2. Word Clouds for Key Themes:
```
from wordcloud import WordCloud

positive_reviews = ' '.join(data[data['sentiment'] == 'Positive']['cleaned_review'])
wordcloud = WordCloud(width=800, height=400).generate(positive_reviews)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.title("Positive Reviews Word Cloud")
plt.show()
```

5. Expected Outcomes

1. Sentiment classification of student feedback into positive, negative, or neutral categories.
2. Insights into the most frequently discussed themes in the feedback.
3. Visualizations of sentiment trends and word clouds for easy interpretation.

6. Additional Suggestions

- Aspect-Based Sentiment Analysis:
- Identify sentiments related to specific features (e.g., content quality, support).
- Time-based Analysis:
- Analyze sentiment trends over time to identify seasonal patterns.
- Deployment:
- Create a dashboard to display real-time sentiment analysis of incoming feedback.

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