Extending a Fuzzy Polarity Propagation Method for Multi-Domain Sentiment Analysis with Word Embedding and POS Tagging

International audienceWithin multi-domain sentiment analysis, we study how different domain-dependent polarities can be learned for the same concepts. To this aim, we extend an existing approach based on the propagation of fuzzy polarities over a semantic graph capturing background linguistic knowledge to learn concept polarities with respect to various domains and their uncertainty from labeled datasets. In particular, we use POS tagging to refine the association between terms and concepts and word embedding to enhance the construction of the semantic graph. The proposed approach is then evaluated on a standard benchmark, showing that the combined use of POS tagging and word embedding improves its performance. One particularly strong point of the proposed approach is its recall, which is always very close to 100%. In addition, we observe that it exhibits good cross-domain generalization capabilities

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 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. 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 relevant training instant is used to assign a polarity and domain label to the new sentence. While the results on well-formed sentences are encouraging, the performance obtained on short texts like tweets demonstrate that more work is needed in this area