In a sequence of articles we compare different NLP techniques to show you how we get valuable information from unstructured text. About a year ago we gathered reviews on Dutch restaurants. We were wondering whether ’the wisdom of the croud’ – reviews from restaurant visitors – could be used to predict which restaurants are most likely to receive a new Michelin-star. Read this post to see how that worked out. We used topic modeling as our primary tool to extract information from the review texts and combined that with predictive modeling techniques to end up with our predictions.

We got a lot of attention with our predictions and also questions about how we did the text analysis part. To answer these questions, we explain our approach in more detail in a series of articles on NLP. But we didn’t stop exploring NLP techniques after our publication, and we also like to share insights from adding more novel NLP techniques. More specifically we will use two types of word embeddings – a classic Word2Vec model and a GLoVe embedding model – we’ll use transfer learning with pretrained word embeddings and we use BERT. We compare the added value of these advanced NLP techniques to our baseline topic model on the same dataset. By showing what we did and how we did it, we hope to guide others that are keen to use textual data for their own data science endeavours.

n a sequence of articles we compare different NLP techniques to show you how we get valuable information from unstructured text. About a year ago we gathered reviews on Dutch restaurants. We were wondering whether ’the wisdom of the croud’ – reviews from restaurant visitors – could be used to predict which restaurants are most likely to receive a new Michelin-star. Read this post to see how that worked out. We used topic modeling as our primary tool to extract information from the review texts and combined that with predictive modeling techniques to end up with our predictions.

We got a lot of attention with our predictions and also questions about how we did the text analysis part. To answer these questions, we explain our approach in more detail in a series of articles on NLP. But we didn’t stop exploring NLP techniques after our publication, and we also like to share insights from adding more novel NLP techniques. More specifically we will use two types of word embeddings – a classic Word2Vec model and a GLoVe embedding model – we’ll use transfer learning with pretrained word embeddings and we use BERT. We compare the added value of these advanced NLP techniques to our baseline topic model on the same dataset. By showing what we did and how we did it, we hope to guide others that are keen to use textual data for their own data science endeavours.

In a sequence of articles we compare different NLP techniques to show you how we get valuable information from unstructured text. About a year ago we gathered reviews on Dutch restaurants. We were wondering whether ’the wisdom of the croud’ – reviews from restaurant visitors – could be used to predict which restaurants are most likely to receive a new Michelin-star. Read this post to see how that worked out. We used topic modeling as our primary tool to extract information from the review texts and combined that with predictive modeling techniques to end up with our predictions.

We got a lot of attention with our predictions and also questions about how we did the text analysis part. To answer these questions, we explain our approach in more detail in a series of articles on NLP. We didn’t stop exploring NLP techniques after our publication, and we also like to share insights from adding more novel NLP techniques. More specifically we will use two types of word embeddings – a classic Word2Vec model and a GLoVe embedding model – we’ll use transfer learning with pretrained word embeddings and we use BERT. We compare the added value of these advanced NLP techniques to our baseline topic model on the same dataset. By showing what we did and how we did it, we hope to guide others that are keen to use textual data for their own data science endeavours.

In a sequence of articles we compare different NLP techniques to show you how we get valuable information from unstructured text. About a year ago we gathered reviews on Dutch restaurants. We were wondering whether ’the wisdom of the croud’ – reviews from restaurant visitors – could be used to predict which restaurants are most likely to receive a new Michelin-star. Read this post to see how that worked out. We used topic modeling as our primary tool to extract information from the review texts and combined that with predictive modeling techniques to end up with our predictions.

We got a lot of attention with our predictions and also questions about how we did the text analysis part. To answer these questions, we will explain our approach in more detail in the coming articles. But we didn’t stop exploring NLP techniques after our publication, and we also like to share insights from adding more novel NLP techniques. More specifically we will use two types of word embeddings – a classic Word2Vec model and a GLoVe embedding model – we’ll use transfer learning with pretrained word embeddings and we use BERT. We compare the added value of these advanced NLP techniques to our baseline topic model on the same dataset. By showing what we did and how we did it, we hope to guide others that are keen to use textual data for their own data science endeavours.

In a sequence of articles we compare different NLP techniques to show you how we get valuable information from unstructured text. About a year ago we gathered reviews on Dutch restaurants. We were wondering whether ’the wisdom of the croud’ – reviews from restaurant visitors – could be used to predict which restaurants are most likely to receive a new Michelin-star. Read this post to see how that worked out. We used topic modeling as our primary tool to extract information from the review texts and combined that with predictive modeling techniques to end up with our predictions.