Model

The technique that was used in this NN is Word2vec. The core concept of using deep learning for natural language processingis is to feed human readable sentences into neural networks so that the models can extract some sort of information from them. Neural networks can take numbers as an input, but not raw text. So we need to find a way to convert these words into a numerical format. The traditional method that can be applied is one hot encoding. But nowadays using word vectors is a much better way to represent textual data for many reasons.

Word2vec is a technique for natural language processing. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. As the name implies, word2vec represents each distinct word with a particular list of numbers called a vector. The vectors are chosen carefully such that a simple mathematical function (the cosine similarity between the vectors) indicates the level of semantic similarity between the words represented by those vectors.

Data Set

Original dataset winemag-data_first150k.csv includes 150000 of wines with variety, location, winery, price, and description. It contains 10 columns and 150k rows of wine reviews. This NN uses a predictive model to identify wines through blind tasting like a master sommelier would. Model predicts wine variety using words in the description/review. The data consists of 10 fields. Points: the number of points WineEnthusiast rated the wine on a scale of 1-100 (though they say they only post reviews for wines that score >=80) Variety: the type of grapes used to make the wine. Description: a few sentences from a sommelier describing the wine's taste, smell, look, feel, etc. Country: the country that the wine is from. Province: the province or state that the wine is from. Region 1: the wine growing area in a province or state. Region 2: sometimes there are more specific regions specified within a wine growing area, but this value can sometimes be blank. Winery: the winery that made the wine. Designation: the vineyard within the winery where the grapes that made the wine are from. Price: the cost for a bottle of the wine.