GitHub - hurun/HotelSpamDetection: Project is to determine whether not only a given hotel review on a website is positive or negative, but if the review was genuine or if it was done by spam or a troll/bot. Purpose is to prevent hotels from artificially inflating their own value or to prevent hotels from being spammed by trolls. Algorithm to implement it was a CNN-LSTM network using feature sets from Doc2Vec, POS tags, dependency tags, and TF-IDF. Trained accuracy on validation set was 93.44%.

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Project is to determine whether not only a given hotel review on a website is positive or negative, but if the review was genuine or if it was done by spam or a troll/bot. Purpose is to prevent hotels from artificially inflating their own value or to prevent hotels from being spammed by trolls. Algorithm to implement it was a CNN-LSTM network us…

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Capstone_project_CNN_LSTM.ipynb		Capstone_project_CNN_LSTM.ipynb
Capstone_project_CNN_LSTM_Transfer_Learning.ipynb		Capstone_project_CNN_LSTM_Transfer_Learning.ipynb
deceptive-opinion-spam-corpus.zip		deceptive-opinion-spam-corpus.zip