Do you want to know what your customers are talking about your airline? What services do they like? And what puts them off? We developed a workflow in Dataiku DSS that uses NLP to determine the sentiment behind the tweets and a webapp that allows the end user to view the results. We walked you through how we used the tweets, did some text cleaning, built models to classify the sentiment, and if time permits - also web scraping to extract airline online reviews.
What's discussed:
- NLP is a field of AI that enables machines to read, understand, and derive meaning from human languages.
- Utilizing a Text Featurization Pipeline to convert text into features of a machine learning model: this includes Preprocess Text (normalize, remove stop words, stem, and tokenize) so "I was running to the river and jumped over a log" is processed to ["i", "run", "river", "jump", "log"].
- Vectorizing the text (converting to numeric features) utilizing either Count Vectorization or Term Frequency-Inverse Document Frequency (TF-IDF).
- Deep dive of recipe reviews in Dataiku DSS.
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