NLP food review classification I have a dataset consisting of a bunch of food reviews. I'm trying to build a model that determines if a given review talks about a food safety issue. The dataset has a lot of features, but relevant to my goal there are only two features of importance --  food reviews and their corresponding numerical ratings.
I'd love a pointer or two to figure out how to approach this problem.
As a first pass attempt, I'm thinking of pipelining all reviews into a sentiment analsyis filter and then look at the ones have keyword related to food safety (eg: &quot;high cholesterol&quot;, &quot;unsafe&quot;, etc)
The best way to solve your problem would be to collect the right labels for your task. Currently, you have the input i.e. text of a review and the rating which can be used to predict the sentiment of the review, but not whether it mentions safety issues. To collect the right labels, you should look at each review and assign it a binary label - 0 means food safety issues are not mentioned, 1 means they are mentioned. The feasibility of such data collection depends on the size of the dataset and the importance of your project. How big is your dataset? How much time/money are you willing to commit to that project? Once you collect the labels, you can train and evaluate a neural network based on them. If this answer helps you, feel free to ask for elaboration, I don't know what's your level of data science expertise and don't want to state the obvious.