Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 767–771, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12 Jos´e Saias DI - ECT - Universidade de ´Evora Rua Rom˜ao Ramalho, 59 7000-671 ´Evora, Portugal jsaias@uevora.pt Abstract This paper describes our participation in SemEval-2015 Task 12, and the opinion min- ing system sentiue. The general idea is that systems must determine the polarity of the sentiment expressed about a certain aspect of a target entity. For slot 1, entity and attribute category detection, our system applies a super- vised machine learning classifier, for each la- bel, followed by a selection based on the prob- ability of the entity/attribute pair, on that do- main. The target expression detection, for slot 2, is achieved by using a catalog of known targets for each entity type, complemented with named entity recognition. In the opin- ion sentiment slot, we used a 3 class polarity classifier, having BoW, lemmas, bigrams after verbs, presence of polarized terms, and punc- tuation based features. Working in uncon- strained mode, our results for slot 1 were as- sessed with precision between 57% and 63%, and recall varying between 42% and 47%. In sentiment polarity, sentiue’s result ac- curacy was approximately 79%, reaching the best score in 2 of the 3 domains. 1 Introduction Social networks and other online platforms are an important communication mechanism in current lifestyle. These platforms aggregate user-generated content, such as opinions that people write and publish freely on the Web, and are now valued for market research and trend analysis. Natural language processing (NLP) helps to automatically extract information from these written opinions. This paper describes a participation in SemEval- 2015 Task 121, Aspect Based Sentiment Analysis (Pontiki et al., 2015), with the sentiue system, from Universidade de ´Evora. In previous editions of SemEval, we participated in Sentiment Analysis (SA) tasks, but in terms of overall polarity, over Twitter messages (Rosenthal et al., 2014), not being aspect oriented. The general idea for this challenge, is that, for a text, the system must determine the polarity of the sentiment expressed about a certain aspect of a particular target entity. Our sentiue system is an evolution from our previous work (Saias and Fernandes, 2013; Saias, 2014), for target oriented SA. Task 12 was run in two phases. In phase A systems are tested for aspect detection with one slot to aspect category, and a second slot for the opinion target expression on the text. Test data includes review texts for two domains: restaurants and laptops. In phase B, aspect category is provided, and systems must assign a polarity (positive, negative, or neutral) for each opinion. In this phase, systems received also texts from a third domain, hotels, for which no sentiment training data was given. We used a supervised machine learning classifier combined with a probability based selection pro- cess, for entity and attribute category detection, on slot 1. Target expression detection …

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