SentNA@ATE_ABSITA: Sentiment Analysis of Customer Reviews Using Boosted Trees with Lexical and Lexicon-based Features

This paper describes our submission to the tasks on Sentiment Analysis of ATE\_ABSITA (Aspect Term Extraction and Aspect-Based Sentiment Analysis). In particular, we focused on Task 3 using an approach based on combining frequency of words with lexicon-based polarities and uses Boosted Trees to predict the sentiment score. This approach achieved a competitive error and, thanks to the interpretability of the building blocks, allows us to show the what elements are considered when making the prediction. We also joined Task 1 proposing a hybrid model that joins rule-based and machine learning methodologies in order to combine the advantages of both. The model proposed for Task 1 is only preliminary.