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

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 502–509, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics SHELLFBK: An Information Retrieval-based System For Multi-Domain Sentiment Analysis Mauro Dragoni Fondazione Bruno Kessler Via Sommarive 18 Povo, Trento dragoni@fbk.eu Abstract 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. 1 Introduction Sentiment analysis is a natural language processing task whose aim is to classify documents according to the opinion (polarity) they express on a given sub- ject (Pang et al., 2002). Generally speaking, sen- timent analysis aims at determining the attitude of a speaker or a writer with respect to a topic or the overall tonality of a document. This task has created a considerable interest due to its wide applications. In recent years, the exponential increase of the Web for exchanging public opinions about events, facts, products, etc., has led to an extensive usage of senti- ment analysis approaches, especially for marketing purposes. By formalizing the sentiment analysis problem, a “sentiment” or “opinion” has been defined by (Liu and Zhang, 2012) as a quintuple: ⟨oj, fjk, soijkl, hi, tl⟩, (1) where oj is a target object, fjk is a feature of the object oj, soijkl is the sentiment value of the opinion of the opinion holder hi on feature fjk of object oj at time tl. The value of soijkl can be positive (by denoting a state of happiness, bliss, or satisfaction), negative (by denoting a state of sorrow, dejection, or disappointment), or neutral (it is not possible to denote any particular sentiment), or a more granular rating. The term hi encodes the opinion holder, and tl is the time when the opinion is expressed. Such an analysis, may be document-based, where the positive, negative, or neutral sentiment is as- signed to the entire document content; or it may be sentence-based where individual sentences are ana- lyzed separately and classified according to the dif- ferent polarity values. In the latter case, it is often desirable to find with a high precision the entity at- tributes towards which the detected sentiment is di- rected. In the …

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