Text Classification Projects | NLP · Sentiment · Spam · Topic

Text Classification Projects for Final Year Students (2026)

Text classification assigns labels to documents, reviews, emails or posts — from binary spam/ham to multi-class news topics and multi-label tagging. Student projects span classical TF-IDF + ML and modern transformer fine-tuning.

This page lists 90+ high-impact topics. Tools include scikit-learn, NLTK, spaCy, TensorFlow/Keras, PyTorch, Hugging Face Transformers and datasets such as IMDb, 20 Newsgroups, SMS Spam and AG News. Ideal for BE, BTech, MTech CS and AI students in Bangalore and across India.

Core Frameworks & Tools

Libraries and models commonly used in academic text classification projects.

scikit-learn NLTK / spaCy BERT / Transformers TensorFlow / Keras PyTorch IMDb · AG News · Spam

Best Text Classification Topics & Tools (90+)

Grouped by theme. Each topic lists primary tools and typical datasets.

#	Project Topic	Tools · Datasets
😊 Sentiment Analysis
1	SentMovie Review Sentiment (IMDb Binary)	TF-IDF + NB/SVM, IMDb
2	SentProduct Review Sentiment Classification	Amazon reviews subset
3	SentTwitter / X Sentiment Analysis	Tweet cleaning, lexicons
4	SentAspect-Based Sentiment Analysis Lite	Aspect + polarity
5	Sent3-Class Sentiment (pos / neu / neg)	SST / custom labels
6	SentLexicon-Based vs ML Sentiment Comparison	VADER vs TF-IDF
7	SentRestaurant Review Sentiment Dashboard	Streamlit + model
8	SentMultilingual Sentiment (English + one more)	Translate or multi model
9	SentEmotion Classification (joy, anger, etc.)	Emotion datasets
10	SentReal-Time Sentiment on Live Text Input	API / Gradio UI
🚫 Spam · Toxicity · Abuse
11	SpamSMS Spam / Ham Classification	SMS Spam Collection
12	SpamEmail Spam Detection Pipeline	Enron / SpamAssassin lite
13	SpamToxic Comment Classification	Jigsaw / Kaggle toxic
14	SpamHate Speech Detection Demo	Public hate datasets
15	SpamPhishing Email Text Classifier	Feature + ML
16	SpamMulti-Label Toxicity (insult, threat, …)	Multi-label metrics
17	SpamCompare NB vs Logistic vs SVM on Spam	scikit-learn suite
18	SpamStreaming Spam Filter Concept	Incremental classifiers
📚 Topic Classification
19	Topic20 Newsgroups Topic Classification	sklearn 20newsgroups
20	TopicBBC News Category Classification	BBC dataset
21	TopicAG News 4-Class Classification	AG News corpus
22	TopicResearch Paper Topic Tagging Lite	Abstract + keywords
23	TopicCustomer Support Ticket Categorization	Custom / Kaggle tickets
24	TopicHierarchical Topic Classification	Parent → child labels
25	TopicTopic Modeling + Classification Hybrid	LDA features + SVM
26	TopicShort-Text Topic Classification (titles)	Title-only experiments
📰 News · Fake News · Credibility
27	NewsFake News Detection Binary Classifier	Fake news datasets
28	NewsNews Source Credibility Scoring Lite	Features + model
29	NewsClickbait Headline Detection	Headline datasets
30	NewsPolitical News Stance Classification	Stance labels
31	NewsMulti-Class News Section Assignment	Sports / tech / biz
32	NewsFact-Check Claim Classification	Claim verification sets
💬 Intent · Dialogue · Chat
33	IntChatbot Intent Classification	Intent utterances
34	IntFAQ Matching / Question Type Classification	FAQ pairs
35	IntComplaint vs Query vs Feedback Labels	Support logs
36	IntMulti-Turn Dialogue Act Classification	Dialogue corpora
37	IntVoice-to-Text Intent Pipeline Demo	STT + classifier
38	IntDomain-Specific Intent (banking / travel)	Custom intents
📊 Classical ML Pipelines
39	MLTF-IDF + Multinomial Naive Bayes	sklearn Pipeline
40	MLTF-IDF + Linear SVM / Logistic Regression	sklearn
41	MLCount Vectorizer vs TF-IDF Comparison	Feature study
42	MLn-gram Features (uni/bi/tri) Ablation	Grid search
43	MLRandom Forest / Gradient Boosting on Text	sklearn ensemble
44	MLFeature Selection for Text (chi2, mutual info)	SelectKBest
45	MLClass Imbalance: SMOTE / Class Weights	imbalanced-learn
46	MLCross-Validation and Hyperparameter Tuning	GridSearchCV
47	MLConfusion Matrix and Error Analysis Report	sklearn metrics
48	MLCalibration of Classifier Probabilities	CalibratedClassifier
🧹 Preprocessing · NLP Basics
49	PreTokenization, Stopwords, Stemming Pipeline	NLTK
50	PreLemmatization with spaCy	spaCy pipeline
51	PreText Cleaning: HTML, URLs, Emojis	regex, clean-text
52	PreLanguage Detection Pre-Filter	langdetect
53	PreDocument Length and Vocabulary Analysis	EDA notebooks
54	PreWord Clouds and Class-wise Keyword Lists	wordcloud, TF
🤖 Deep Learning · BERT · Transformers
55	BERTBERT Fine-Tuning for Binary Classification	Hugging Face, IMDb
56	BERTDistilBERT / TinyBERT for Efficiency	Transformers library
57	BERTMulti-Class News with Fine-Tuned BERT	AG News + BERT
58	BERTLSTM / BiLSTM Text Classifier	Keras / PyTorch
59	BERTCNN for Text Classification (Kim-style)	1D conv over embeddings
60	BERTWord2Vec / GloVe + Classifier Pipeline	Gensim embeddings
61	BERTCompare Classical vs BERT on Same Task	Accuracy / latency table
62	BERTDomain Adaptation: Fine-Tune on Domain Text	Continued pretrain lite
63	BERTRoBERTa / ALBERT Experiment Notes	HF model cards
64	BERTAttention Visualization for Interpretability	BertViz concepts
🏷️ Multi-Label · Hierarchical
65	MultiMulti-Label Text Classification	One-vs-rest, Hamming
66	MultiTag Prediction for Stack Overflow Style	Multi-label F1
67	MultiHierarchical Classification Metrics	Tree-aware scores
68	MultiLabel Correlation Analysis	Co-occurrence matrix
🏢 Domain Applications
69	AppLegal Document Type Classification	Legal text samples
70	AppMedical Abstract MeSH / Category Lite	PubMed abstracts
71	AppResume / Job Category Classification	Job description sets
72	AppE-commerce Product Category from Title	Product catalogs
73	AppBug Report Severity Classification	Issue trackers
74	AppCode Comment / Documentation Classification	Source comment labels
75	AppStudent Feedback Sentiment + Theme	Course surveys
76	AppFinancial News Sentiment for Stocks Lite	Headlines + labels
🔬 Advanced · Evaluation · Deployment
77	AdvActive Learning for Label Efficiency	Uncertainty sampling
78	AdvData Augmentation: Back-Translation / EDA	nlpaug concepts
79	AdvAdversarial Text Robustness Demo	Character / word attacks
80	AdvModel Distillation for Faster Inference	Teacher → student
81	AdvONNX / Quantization for Deployment	ONNX Runtime
82	AdvStreamlit / Gradio Classification App	Web UI
83	AdvFastAPI Prediction Service	REST endpoint
84	AdvExplainability: LIME / SHAP on Text	lime, shap
85	AdvBias and Fairness Audit Across Groups	Stratified metrics
86	AdvCross-Lingual Classification Transfer	Multilingual BERT
87	AdvFew-Shot Classification with Prompting Lite	Prompt templates
88	AdvBenchmark Suite: 3 Datasets × 3 Models	Unified eval script
89	AdvEducational Lab: Bag-of-Words → BERT	Curriculum path
90	AdvEnd-to-End: Clean → Train → Evaluate → Deploy → Report	Full pipeline package
91	AdvReproducibility: Seeds, Configs, Experiment Log	YAML + tracking
92	AdvThesis Package: Methods, Ablations, Results, Discussion	Full documentation

Topics reflect NLP and text classification academic practice with open-source tools and public data. Contact us for pipelines, evaluation metrics, university-format report, PPT and viva Q&A for any topic above.

Why Choose Us for Text Classification Projects?

Bangalore-based guidance for BE, BTech and MTech students working on sentiment, spam, topic and transformer models.

Sentiment Analysis

Review and social sentiment with classical ML and lexicon baselines.

Spam & Toxicity

SMS/email spam and toxic comment classification with clear metrics.

Topic & News

Newsgroups, AG News and fake-news style multi-class problems.

BERT & Transformers

Fine-tuning and comparison of classical vs transformer pipelines.

Frequently Asked Questions — Text Classification

Top topics include sentiment analysis on reviews, spam/ham email classification, news topic categorization, fake news detection, intent classification for chatbots, multi-label classification and BERT/transformer fine-tuning on domain datasets.

scikit-learn (TF-IDF, Naive Bayes, SVM), NLTK/spaCy, TensorFlow/Keras, PyTorch, Hugging Face Transformers (BERT, DistilBERT); datasets include IMDb, 20 Newsgroups, SMS Spam, AG News, SST and Kaggle text sets.

Yes. Packages include preprocessing pipelines, model training notes, evaluation metrics (accuracy, F1, confusion matrix), university-format report, PPT and viva Q&A.

Classical pipelines (TF-IDF + Naive Bayes/SVM) work well on smaller datasets and are easy to explain. Transformers (BERT) often yield higher accuracy on complex language but need more data and compute; many student projects compare both.

Text Classification Lab — Bangalore

Sentiment, spam, topic and transformer setups for BE, BTech and MTech NLP projects.

Sentiment
Analysis

IMDb / Reviews

Spam
Detection

SMS / Email

Topic
Classification

20 Newsgroups

Fake News
Detection

Binary / Multi

BERT
Fine-Tuning

Transformers

Classical ML
TF-IDF + SVM

scikit-learn

Intent
Classification

Chatbot

Viva & Report
Preparation

Documentation

Text Classification Projects.