01. Machine Learning
Paste a hotel review and the model computes its perplexity under two language models trained on truthful vs. deceptive reviews. The lower-perplexity model wins.
Ctrl+Enter to classify
Two unigram language models are trained: one on 256 truthful hotel reviews, one on 256 deceptive reviews. Both use Laplace (add-1) smoothing to handle unseen words.
For a new review, perplexity is computed under each model. Perplexity measures how surprised the model is by the text. A truthful-trained model will assign lower perplexity to genuinely truthful reviews and vice versa.
The verdict is determined by which model assigns lower perplexity. Training data: Chicago Opinion Spam Dataset (Ott et al. 2011).