This data-driven project that collects and analyzes employee review from careerbliss using web scraping and APIs . it includes sentiment analysis, topic modeling, and classification to uncover key workplace insights from employee feedback.
- Scrape real-world employee reviews.
- Clean and preprocess text data.
- Perform sentiment analysis and classification.
- Identify dominant themes using topic modeling.
- Visualize results for HR insights and decision-making.
- Web Scraping: Selenium
- NLP and ML: NLTK, TextBlob, Vader, Scikit-learn, Gensim, TF-IDF
- Visualization: Matplotlib, Seaborn, WordCloud
- Work-life balance, supportive team, and career growth were common pros.
- Poor management, long hours, and lack of benefits emerged as frequent cons.
- Sentiment analysis showed an overall balanced view, with insights per review.
- Machine learning models accurately classified sentiment with meaningful performance.
- Topic modeling highlighted major themes in employee feedback.