Today's project was to build a DL computational linguistics model using word2vec to accurately classify text in a sentiment analysis paradigm. Our hypothetical use case was to apply DL to enable the management of a restaurant chain to understand the general sentiment of text responses their customers made, in response to a phone text question asking about their experience after dining. Our specific task was to build the natural language processing model that would create business intelligence from the data obtained in this simple (hypothetical) application.
Revisit our success criteria: How did we do? Did we succeed? What is the impact of success? Just as we defined success at the beginning of the project, these are the key questions we ask as DL data scientists as we look to wrap up a project.
Our CNN model, which was built on the trained word2vec model created earlier...
