This final project focuses on the use of Natural Language Processing (NLP) techniques and data visualization to analyze mental health-related text data. By applying sentiment analysis to a dataset of user reviews, the project aims to classify texts into positive, negative, and neutral sentiments. Additionally, advanced preprocessing techniques, such as stopword removal and stemming, were applied to cleanse and prepare the text for further analysis.
The project employs WordCloud visualizations to display frequent terms for each sentiment category and uses Seaborn to generate bar plots that highlight the most common words in each sentiment. The analysis was performed on a dataset that included various reviews and feedback related to mental health, which was classified into different sentiment categories. The project also offers a powerful way of visualizing sentiment distributions, helping to understand the emotional tone of large datasets.
The primary objective of this project was to gain insights into public sentiment regarding mental health issues, with a focus on building an intuitive, data-driven approach to analyze sentiment and generate actionable insights from textual data.
Tools :
- Python for data manipulation and analysis.
- NLTK for Natural Language Processing tasks like tokenization, stopword removal, and stemming.
- Seaborn for data visualization, including bar plots.
- WordCloud for visualizing frequent terms by sentiment.
- Pandas for data handling and preprocessing.
- Matplotlib for basic plotting and visualization.
- Jupyter Notebook for prototyping and exploration.