SHELLFBK: An Information Retrieval-based System For Multi-Domain Sentiment Analysis

This paper describes the SHELLFBK system that participated in SemEval 2015 Tasks 9, 10, and 11. Our system takes a supervised approach that builds on techniques from in-formation retrieval. The algorithm populates an inverted index with pseudo-documents that encode dependency parse relationships ex-tracted 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. When the polarity or domain of a new sentence has to be computed, the new sentence is converted to a query that is used to retrieve the most similar sentences from the training set. The retrieved instances are scored for relevance to the query. The most rele-vant training instant is used to assign a polarity and domain label to the new sentence. While the results on well-formed sentences are en-couraging, the performance obtained on short texts like tweets demonstrate that more work is needed in this area.