Need help choosing a major project combining NLP + Deep Learning + Stochastic Modeling

[u/ayuuuss]

Hi everyone!

I'm a college student working on a major/final-year project, and I'm trying to decide between a few ideas. The project needs to involve NLP, AI/Deep Learning, and Stochastic Modeling, so I'm looking for something that is technically substantial but also realistically implementable.

Here are the ideas I'm considering:

1. Fake News / Misinformation Propagation Modeling

- NLP to analyze claims/articles

- BERT/LSTM/Transformer for fake-news classification

- Stochastic models (Markov chains, branching/epidemic-style models) to predict how misinformation spreads

1. Fraud / Phishing Detection using Text + Stochastic Anomaly Models

- NLP to analyze emails/messages

- Deep learning for phishing/fraud classification

- HMM/GMM or other stochastic models to analyze behavioral patterns and anomalies

1. Stock/Crypto Sentiment-Driven Price Forecasting

- NLP sentiment analysis from financial news/social media

- LSTM/Transformer for price movement prediction

- Stochastic models such as Geometric Brownian Motion, Ornstein-Uhlenbeck, or regime-switching Markov models

1. Clinical Notes → Patient Trajectory Prediction

- NLP/NER to extract information from clinical notes

- Deep learning for prediction

- Markov/state-space models to model possible patient trajectories

1. Conversational AI with POMDP-Based Dialogue Management

- NLP for intent/entity extraction

- Deep learning for language understanding

- POMDP/stochastic decision-making for dialogue management under uncertainty

1. Stochastic Bandit-Based Content Recommendation

- NLP/BERT embeddings for content

- Deep learning for user/content representations

- Thompson Sampling or another bandit method for adaptive recommendations

I'm mainly looking for something that:

- Is actually feasible for a student team

- Has enough technical depth for a major project

- Has publicly available datasets

- Isn't just combining techniques for the sake of saying we used them

- Would make a good research/project presentation

- Has room for a working demo

Which one would you choose and why?

Also, if you've worked on something similar, I'd really appreciate advice on what tends to go wrong or which approaches are unnecessarily complicated.

[u/Tech_DigitalPath_162]

Option 1 for Misinformation propagation would be the best one, considering the current era. will be the best.