Subject: Data Analysis
Topic: Sentiment Analysis of Product Reviews
Objective: The goal of this assignment is to perform sentiment analysis on product reviews to determine the sentiment of the reviewer towards the product.
Instructions:
1. Data Preparation:
- Gather a dataset of product reviews from an appropriate source (e.g., Amazon, Yelp).
- Clean the data by removing duplicate reviews, handling missing values, and converting the text to lowercase.
2. Exploratory Data Analysis:
- Explore the data to understand its characteristics and distribution.
- Perform basic statistics, such as frequency counts and word clouds, to identify common words and phrases used in the reviews.
3. Sentiment Analysis:
- Use a suitable sentiment analysis library or tool (e.g., TextBlob, VADER, or spaCy) to assign sentiment scores to each review.
- Group the reviews into positive, negative, or neutral categories based on their sentiment scores.
4. Feature Engineering:
- Extract relevant features from the reviews that might contribute to the sentiment. These could include word frequencies, punctuation marks, or other NLP-related features.
5. Machine Learning Model:
- Develop a supervised machine learning model to classify the reviews as positive or negative.
- Train the model on the labeled data and evaluate its performance using appropriate metrics (e.g., accuracy, precision, recall, and F1-score).
6. Model Interpretation:
- Visualize the model's predictions using confusion matrices or other relevant visualizations.
- Analyze the misclassified reviews to identify areas for improvement.
7. Reporting:
- Write a report summarizing the findings of the sentiment analysis.
- Include details about the data preparation, exploratory data analysis, feature engineering, model training, and evaluation results.
Submission:
- Submit the following:
- A Jupyter Notebook or Python script containing your code and analysis.
- A PDF report summarizing the findings.
Deadline:
- The assignment is due on [date].
- Late submissions will incur a penalty of 10% per day.