Text Classification Projects You Can Build This Weekend

Text classification is one of the foundational tasks in natural language processing (NLP) and machine learning. It involves categorising text into organised groups based on its content. From spam detection in emails to sentiment analysis on social media, text classification helps computers understand human language and make informed decisions.

If you’re learning data science or planning to enhance your skills, working on text classification projects is a great way to practice real-world applications. Whether you’re a beginner or an intermediate learner, these projects can be completed over a weekend and will add valuable experience to your portfolio.

If you’re looking to kickstart your journey, enrolling in a data science course in Mumbai can provide you with the structured guidance and tools necessary to tackle such projects effectively.

In this blog, we will explore some practical text classification projects you can build this weekend, how to approach them, and what tools you might need. Let’s dive in!

Why Text Classification?

Text classification is everywhere — in customer support to filter tickets, in healthcare to categorise patient notes, or in finance to flag fraudulent messages. The benefits of mastering text classification include:

• Automating tedious manual processes
• Extracting actionable insights from extensive text data
• Enabling personalised user experiences
• Improving decision-making through data-driven methods

By building small but meaningful text classification projects, you can get hands-on experience in preprocessing text, selecting features, training models, and evaluating their performance. If you want to get expert guidance and build these projects with detailed mentorship, enrolling in a data scientist course will help you gain that edge and prepare you for a successful career in AI and machine learning.

Project 1: Spam Email Classifier

Objective: Classify emails as spam or not spam.

This is one of the classic NLP tasks and a great starter project. You can use datasets like the Enron Spam Dataset or the SMS Spam Collection Dataset to train your model.

Steps:

1. Data collection: Download a labelled dataset containing spam and ham (non-spam) emails or messages.
2. Preprocessing: Clean the text by removing punctuation, numbers, and stopwords. You may also want to apply stemming or lemmatisation.
3. Feature extraction: Convert text into numerical form using methods such as TF-IDF or word embeddings like Word2Vec.
4. Model training: Train classification algorithms like Logistic Regression, Naive Bayes, or Support Vector Machines (SVM).
5. Evaluation: Use accuracy, precision, recall, and F1-score to assess model performance.
6. Deployment: You can create a simple user interface that allows users to input text and get spam predictions.

Tools: Python, Scikit-learn, NLTK, Pandas

Project 2: Sentiment Analysis on Movie Reviews

Objective: Classify movie reviews as positive, negative, or neutral.

Sentiment analysis helps businesses monitor customer opinions and adjust strategies accordingly. The IMDb dataset or Twitter sentiment datasets are popular for this.

Steps:

1. Data acquisition: Use open datasets like IMDb movie reviews or collect tweets about a trending topic.
2. Preprocessing: Clean the text and handle emojis, slang, or abbreviations commonly found in social media text.
3. Feature engineering: Use TF-IDF or word embeddings. Alternatively, explore pretrained models like BERT for better accuracy.
4. Modelling: Use algorithms such as Random Forest, Gradient Boosting, or fine-tune transformer models.
5. Testing: Evaluate the model with confusion matrices and ROC curves.
6. Application: Build a dashboard that shows sentiment trends over time or by category.

Tools: Python, TensorFlow/PyTorch, Hugging Face Transformers

Project 3: News Article Categorisation

Objective: Categorise news articles into topics like sports, politics, technology, and entertainment.

This project is highly relevant for news aggregators or recommendation engines.

Steps:

1. Dataset: Use the Reuters news dataset or scrape news websites for labelled articles.
2. Text cleaning: Normalise text, remove HTML tags if scraping, and process stopwords.
3. Vectorisation: Use CountVectorizer or TF-IDF Vectorizer.
4. Classification: Try Naive Bayes, SVM, or deep learning classifiers.
5. Model validation: Use cross-validation to check model robustness.
6. User interface: Create an app that fetches news and instantly predicts its category.

Tools: Python, Scikit-learn, BeautifulSoup (for scraping), Flask (for app)

Project 4: Customer Support Ticket Classification

Objective: Automatically classify support tickets like billing issues, technical problems, or feature requests.

This can significantly improve ticket routing efficiency.

Steps:

1. Data: Gather or simulate a dataset of support tickets with categories.
2. Text preparation: Clean text, correct typos, and standardise terminology.
3. Feature extraction: Use TF-IDF or word embeddings.
4. Model development: Test multiple classifiers to find the best fit.
5. Performance: Measure accuracy and the impact on response time improvement.
6. Integration: Connect the model to a support system for live classification.

Tools: Python, Scikit-learn, FastText

Project 5: Toxic Comment Detection

Objective: Classify online comments as toxic or non-toxic to moderate communities.

This is vital for maintaining healthy online interactions.

Steps:

1. Dataset: Use the Jigsaw Toxic Comment Classification Challenge dataset.
2. Preprocessing: Remove special characters and URLs, and normalise text.
3. Feature engineering: Use embeddings from pretrained models like GloVe or BERT.
4. Modelling: Use deep learning models such as LSTM, GRU, or transformers.
5. Evaluation: Focus on recall and precision to avoid misclassifying benign comments.
6. Deployment: Implement a moderation bot or dashboard for community managers.

Tools: Python, Keras/TensorFlow, Hugging Face

How to Approach These Projects?

1. Start Small: Begin with simple models like Naive Bayes or Logistic Regression. These require less computation and can provide a baseline.
2. Data Preprocessing: This step often takes the most time. Clean, normalise, and prepare your data carefully.
3. Experiment: Try different feature extraction methods and algorithms. Use cross-validation to avoid overfitting.
4. Evaluate Thoroughly: Use multiple metrics, not just accuracy.
5. Iterate: Improve your model by tuning hyperparameters and adding new features.
6. Document: Keep your work organised and well-documented for future reference or sharing.

Why Build These Projects?

Text classification projects are not only fun but also very relevant in the current data-driven world. Whether you’re a student or a professional upskilling yourself, hands-on projects help reinforce concepts, build confidence, and showcase your capabilities to potential employers.

If you want to accelerate your learning and gain expert mentorship, consider enrolling in a data science course in Mumbai. Such courses often include project-based learning, giving you access to real datasets, tools, and guidance from industry experts.

Tools and Libraries to Use

• Python: The most popular language for text classification tasks.
• NLTK &SpaCy: Libraries for preprocessing and linguistic features.
• Scikit-learn: For classic machine learning algorithms.
• TensorFlow&PyTorch: For deep learning models.
• Hugging Face Transformers: Access to state-of-the-art pretrained NLP models.
• Jupyter Notebook: Interactive environment for development and experimentation.

Final Thoughts

Text classification is a versatile and powerful technique that can be applied to a wide range of problems. The projects listed above can be tackled within a weekend and will give you a strong foundation in NLP workflows. As you work through these projects, you’ll learn valuable skills such as data cleaning, feature extraction, model training, and evaluation.

Remember, the key to mastering text classification and NLP lies in continuous practice and learning. Taking up a data scientist course can provide you with a structured learning path, industry insights, and a supportive community.

So, gear up, pick a project that excites you, and start building your text classification solution today!

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