This project involves working on the "blogs.csv" dataset, which contains blog posts categorized into various themes. The main objectives are to build a text classification model using the Naive Bayes algorithm and perform sentiment analysis to understand the general sentiment (positive, negative, neutral) expressed in these posts. This project enhances the understanding of text classification, sentiment analysis, and the practical application of the Naive Bayes algorithm in Natural Language Processing (NLP).
The dataset, "blogs.csv", consists of blog posts along with their associated categories. Each row represents a blog post with the following columns:
- Data: The content of the blog post.
- Labels: The category to which the blog post belongs.
- Load the "blogs.csv" dataset and perform exploratory data analysis to understand its structure and content.
- Preprocess the data by cleaning the text (removing punctuation, converting to lowercase, etc.), tokenizing, and removing stopwords.
- Perform feature extraction to convert text data into a format that can be used by the Naive Bayes model, using techniques such as TF-IDF.
- Split the data into training and test sets.
- Implement a Naive Bayes classifier to categorize the blog posts into their respective categories. Libraries like scikit-learn can be used for this purpose.
- Train the model on the training set and make predictions on the test set.
- Choose a suitable library or method for performing sentiment analysis on the blog post texts.
- Analyze the sentiments expressed in the blog posts and categorize them as positive, negative, or neutral. Consider only the Data column and get the sentiment for each blog.
- Examine the distribution of sentiments across different categories and summarize your findings.
- Evaluate the performance of your Naive Bayes classifier using metrics such as accuracy, precision, recall, and F1-score.
- Discuss the performance of the model and any challenges encountered during the classification process.
- Reflect on the sentiment analysis results and their implications regarding the content of the blog posts.
- Your submission should include a comprehensive report and the complete codebase.
- Your code should be well-documented and include comments explaining the major steps.
- Correct implementation of data preprocessing and feature extraction.
- Accuracy and robustness of the Naive Bayes classification model.
- Depth and insightfulness of the sentiment analysis.
- Clarity and thoroughness of the evaluation and discussion sections.
- Overall quality and organization of the report and code.
This project is licensed under the MIT License - see the LICENSE file for details.