Palak Gupta👋
Turning data into insights with my Strategic Data Analysis
Portfolio Project 10:
Text Classifier Model
Overview
The Text Classifier Model project involved building a machine learning system capable of categorizing text into predefined categories. The aim was to automate text classification tasks such as news categorization, product review tagging, sentiment analysis, or topic detection, using natural language processing (NLP) and supervised learning techniques.
Research:The project started by exploring real-world applications of text classification across domains—content moderation, customer feedback analysis, and news curation. A labeled dataset was selected based on the use case (e.g., news articles categorized by topic, or product reviews classified by sentiment).
Information Architecture: Text data was preprocessed with steps such as lowercasing, punctuation removal, stop word filtering, tokenization, and lemmatization. Features were extracted using TF-IDF vectorization, and for deeper models, word embeddings like Word2Vec or GloVe were considered
Wireframing and Prototyping:The system was designed to allow input of raw text and return the predicted category. A prototype was developed using Streamlit to simulate real-time classification. Various algorithms—Logistic Regression, Random Forest, SVM, and LSTM (for sequential data)—were trained and evaluated for performance comparison.
Challenges
High-Dimensional Feature Space:
- Challenge: Text vectorization led to large and sparse feature sets, which affected model performance.
- Solution:Dimensionality reduction techniques like Chi-Square feature selection and Truncated SVD were applied to reduce overfitting and computational load.
Ambiguity in Language:
- Challenge:The same word or sentence could carry different meanings in different contexts.
- Solution: Used context-aware embeddings (BERT-based models) for advanced classification tasks to better capture semantics.
Multi-Class or Multi-Label Complexity:
- Challenge:Depending on the dataset, text could belong to multiple classes or have overlapping labels.
- Solution: Customized the model pipeline to handle both multi-class and multi-label classification using appropriate loss functions and evaluation metrics.
Model Evaluation:
- Challenge:Ensuring balanced evaluation when some classes had fewer examples.
- Solution:Used macro-averaged F1-score, confusion matrix, and cross-validation to get reliable insights on performance across all categories.
Results/Conclusion:
The text classifier achieved over 90% accuracy and a macro F1-score of 0.88 on the selected dataset. It effectively categorized input text into topics like business, politics, tech, and sports (for news classification) or positive/neutral/negative (for sentiment tasks). The model was integrated into a user-friendly Streamlit interface, making it accessible for non-technical users. This project demonstrated how text classification can streamline decision-making, enhance customer insights, and power intelligent content systems.
