In questo meeting aperto a persone con ogni tipo di background, Debora Nozza ci introdurrà al natural language processing, con particolare attenzione a semantic analysis tramite questo paper del 2002. Verranno anche proposti esempi pratici e live coding.

Short Bio. Debora Nozza è post-doctoral researcher presso l'Università di Milano Bicocca. Le sue aree di ricerca sono Machine Learning, Natural Language Processing, Deep Learning e Sentiment Analysis.

Full-Text of the Paper. Full Text on ACM DL

Original Abstract.

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., “subtle nuances”) and a negative semantic orientation when it has bad associations (e.g., “very cavalier”). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word “excellent” minus the mutual information between the given phrase and the word “poor”. A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.