NLP Projects with Source Code

NLP Projects Across Every Text Task

Text data is messy — different encodings, slang, abbreviations, typos, and context-dependent meaning. Our NLP projects handle this reality with proper preprocessing pipelines that clean, tokenize, and encode text correctly.

Text Classification and Sentiment Analysis

Classification projects categorize news articles, detect spam emails, classify customer support tickets, and analyze product review sentiment. You'll see implementations using both traditional approaches (TF-IDF + Logistic Regression) and deep learning (BERT fine-tuning). Comparison code shows you when the simpler approach is good enough.

Chatbots and Conversational AI

Chatbot projects range from rule-based bots with intent matching to transformer-based conversational agents. Intent-based bots use NLTK for tokenization and scikit-learn for intent classification. Advanced bots use DialoGPT or fine-tuned T5 models. Each chatbot includes a web interface for live conversation.

Named Entity Recognition (NER)

NER projects extract person names, organizations, locations, dates, and custom entities from text. These use spaCy's NER pipeline, BiLSTM-CRF architectures, or fine-tuned BERT models. Projects include annotated datasets and scripts for training on your own entity types.

Text Summarization and Generation

Summarization projects implement both extractive (selecting key sentences) and abstractive (generating new text) approaches. They handle news articles, research papers, and long documents. Generation projects use GPT-2 or T5 models fine-tuned for specific domains.

NLP Preprocessing Pipelines

Text preprocessing is where most NLP projects go wrong. Our code handles tokenization, stop word removal, lemmatization, and encoding in a pipeline class you can reuse. For transformer-based projects, the Hugging Face tokenizer handles subword tokenization automatically. Each project's preprocessing matches what the model expects.

Choosing Between Classical and Deep Learning NLP

For many text classification tasks, TF-IDF with a linear model gets you 85-90% accuracy with training times under a minute. BERT gets you 92-95% but takes hours to fine-tune. Our projects include both approaches so you can make an informed choice based on your accuracy requirements and compute budget.

Evaluation for NLP Tasks

NLP metrics go beyond accuracy. Our projects report precision, recall, F1-score per class, confusion matrices, and for NER projects, entity-level evaluation. For chatbots, we include human evaluation scripts and BLEU score computation. These evaluation outputs are ready for your project report.