ood and nutrition in general are important aspects of everybody’s life. The way you eat
determines how you feel, what illnesses you might suffer in the future and it even talks
about your social background.
In this study, we use Twitter as a source of nutritional information, capturing tweets that
talk about food. The goals of this project are designing and developing a tool capable of
performing nutritional analysis over the population, as well as developing a classifier that
distinguishes between healthy and unhealthy dishes. The project consists of the following
stages: capture, classifier development and nutrition analysis system building.
In the first stage we collected tweets 19773 in Spanish and Catalan containing meta data
such as geographic locations and user names, and conformed a corpus for our research.
In the second stage we built a system to preprocess these tweets transforming them into a
source of information for predictive models and visualizations. With that purpose we ex-
tracted features as nutrients contained by the food mentioned in those tweets. This features
were used as input for different classifiers which were evaluated using various metrics.
The classifier had to determine if the food mentioned in the tweet was healthy or not.
The classifier that gave the best performance was the one implemented with the K Nearest
Neighbors algorithm, reaching an accuracy and f1-score of 0,93 and 0,93 respectively.
In the final stage we developed a nutrition analysis service that allows you to visualize
the analysis of the nutrition of a certain population being capable of filtering the tweets
between Autonomous Communities, gender, the health label given by the classifier and the
hour of creation.