User Reviews Analysis Model - Nordvtech Ltd

Designed and implemented a sentiment analysis model leveraging NLP techniques to analyze customer reviews across various platforms. This model identifies user sentiment—positive, negative, or neutral—providing businesses with actionable insights to enhance customer experience and inform decision-making processes.

Key Achievements:

• Data Gathering: Collected user reviews from online sources and social media, ensuring a rich and diverse dataset for analysis.
• Text Processing: Employed NLTK and spaCy for text normalization, stemming, and lemmatization to prepare data for analysis.
• Sentiment Classification: Developed machine learning models using TensorFlow to classify sentiments, employing LSTM networks to capture sequential dependencies in text.
• Evaluation Metrics: Achieved an accuracy of 91% and an F1 score of 0.88, demonstrating robust model performance across diverse review types.
• Visualization: Utilized Matplotlib and Seaborn for data visualization, enabling clear representation of sentiment trends over time.

This model empowered the business to understand customer sentiment better, leading to strategic improvements in products and services based on user feedback.

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