Backend:
- Investigated SVMs and logistic regression classifier much deeper
- Finalised our statistical model, at 74.5% accuracy, using reviewer features and SVC with grid search
- Investigated three forms of neural network architectures, FFNN, CNN and RNN, in a POC fashion
- Cross compared the performance of the these three architectures using BOW and word2vec
- Created custom word embeddings over our datasets using Google's word2vec (attached) and Facebook's fastText
- Did an experiment investigating FFNN architectures with BOW and word2vec
- Created and hosted our first neural network model (attached), a FFNN running alongside our SVM returning feature weights
- Remodelled and revamped the wiki for documenting
- Toyed around and read up on Grove, DCU's GPU instances that we will use next semester to train models
- Researched deep learning and neural networks extensively and documented our research in the wiki
Frontend:
- Got rejected by the Yelp API, however..
- Integrated with Google Places API, and used an ensemble of Yelp Fusion and Google Places to return Google reviews
- Set up a NoSQL OO database on our Yelp dataset to make our data queryable, allowing us pseudo-Yelp access as a backup
- Did extensive research on data visualization and color theory, documented in the wiki
- Implemented a word cloud indicating the most important words to a particular classification
- Grouped best and worst classified reviews to make the result easier to read