NLP Projects – Practical Ideas and Starter Code

Build a sentiment classifier that labels reviews as positive, negative or neutral using a dataset from e-commerce or app store reviews.

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

# Load a sample sentiment dataset
dataset = load_dataset("imdb", split="test[:200]")

model_name = "distilbert-base-uncased-finetuned-sst-2-english"
clf = pipeline("sentiment-analysis", model=model_name)

text = dataset[0]["text"]
print(text[:200])
print(clf(text))

Create a simple FAQ chatbot that answers common customer questions using retrieval-based QA or a small language model.

from transformers import pipeline

qa = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")

context = \"\"\"Nikhil LearnHub provides tutorials on AI, NLP, machine learning and data science
with practical examples and interview questions.\"\"\"

question = "What does Nikhil LearnHub provide?"
print(qa({"question": question, "context": context}))

Train a classifier that assigns topics like politics, sports, technology or business to news headlines or full articles.

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

data = fetch_20newsgroups(subset="train", remove=("headers", "footers", "quotes"))
vectorizer = TfidfVectorizer(max_features=5000)
X = vectorizer.fit_transform(data.data)

clf = LogisticRegression(max_iter=1000)
clf.fit(X, data.target)

print("Train accuracy:", clf.score(X, data.target))

Automatically route support tickets to the right team (billing, technical, sales) based on the ticket description.

Use topic modeling to discover themes in a collection of research abstracts or blog posts.

from gensim import corpora, models
from nltk.tokenize import word_tokenize

documents = [
    "Deep learning for natural language processing",
    "Topic modeling discovers themes in documents",
    "Neural networks and transformers in NLP"
]

texts = [word_tokenize(doc.lower()) for doc in documents]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

lda = models.LdaModel(corpus, num_topics=2, id2word=dictionary, passes=10)
for idx, topic in lda.print_topics(num_topics=2, num_words=4):
    print(f"Topic {idx}: {topic}")

Build a summarization tool that creates short summaries for news articles, reports or long blog posts.

from transformers import pipeline

summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

article = "Natural Language Processing (NLP) is a field of AI that focuses on making sense of human language..."
summary = summarizer(article, max_length=60, min_length=20, do_sample=False)
print(summary[0]["summary_text"])

Experiment with translating short sentences between English and another language using pretrained translation models. Start with off-the-shelf sequence-to-sequence transformers, then later fine-tune on a small parallel corpus to adapt to your domain.

From an engineering perspective, this project teaches you how to handle tokenization for two languages, manage maximum sequence lengths and evaluate translation quality with BLEU or COMET scores. You can wrap your model behind a simple web form or API that accepts input text and returns translated output.

Extract entities like names, skills, companies and dates from resumes or LinkedIn profiles to simplify HR workflows. You can start with a general-purpose NER model and then iteratively add custom labels such as SKILL, TOOL or CERTIFICATION.

This project exposes you to annotation or weak labeling, span-level evaluation (precision/recall/F1) and the challenges of domain adaptation. A simple UI can highlight detected entities in color, helping recruiters quickly scan key information.

Allow users to ask questions over a set of documents (PDFs, articles) by combining retrieval and a QA model. First index your documents with dense embeddings, then retrieve the most relevant chunks and feed them into a reading comprehension model.

This kind of retrieval-augmented QA system is very close to real-world “ask your documents” products. It teaches you how to chunk long documents, store vectors efficiently and handle failure cases when the answer is not present in the context.

Detect and filter toxic or abusive comments in online forums or social media feeds. You can start with public datasets like Kaggle’s Jigsaw toxicity data and fine-tune a transformer classifier on labels such as toxic, severe toxic, obscene or insult.

Beyond model training, this project forces you to think about fairness, false positives and deployment: how to integrate the model into a moderation workflow, log decisions and allow human review for borderline cases.