An Information Retrieval-Based System for Multi-domain Sentiment Analysis

This paper describes the SHELLFBK system that participated in ESWC 2015 Sentiment Analysis challenge. Our system takes a supervised approach that builds on techniques from information retrieval. The algorithm populates an inverted index with pseudo-documents that encode dependency parse relationships extracted from the sentences in the training set. Each record stored in the index is annotated with the polarity and domain of the sentence it represents; this way, it is possible to have a more fine-grained representation of the learnt sentiment information. When the polarity of a new sentence has to be computed, the new sentence is converted to a query and a two-steps computation is performed: firstly, a domain is assigned to the sentence by comparing the sentence content with domain contextual information learnt during the training phase, and, secondly, once the domain is assigned to the sentence, the polarity is computed and assigned to the new sentence. Preliminary results on an in-vitro test case demonstrated promising results.