UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis
Giuseppe Castellucci, Simone Filice, Danilo Croce, Roberto Basili · Cineca Institutional Research Information System (Tor Vergata University) · 2013
In this paper, the UNITOR system participating in the SemEval-2013 Sentiment Analysis in Twitter task is presented. The polarity detection of a tweet is modeled as a classification task, tackled through a Multiple Kernel approach. It allows to combine the contribution of complex kernel functions, such as the Latent Semantic Kernel and Smoothed Partial Tree Kernel, to implicitly integrate syntactic and lexical information of annotated examples. In the challenge, UNITOR system achieves good results, even considering that no manual feature engineering is performed and no manually coded resources are employed. These kernels in-fact embed distributional models of lexical semantics to determine expressive generalization of tweets.