This paper describes the participation of the SINAI research group in Task 4 of the 2014 edition of the International Workshop SemEval. This task is concerned with Aspect Based Sentiment Analysis and its goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect.
This article presents the participation of the SINAI research group in the task Sentiment Analysis in Twitter of the SemEval Workshop. Our proposal consists of a voting system of three polarity classifiers which follow a lexicon-based approach.
An electronic voting (e-voting) system is a voting system in which the election data is recorded, stored and processed primarily as digital information. There are two types of e-voting: on-line, e.g. via Internet, and offline, by using a voting machine or an electronic polling bo
To help users quickly understand the major opinions from massive online reviews, it is important to automatically reveal the latent structure of the aspects, sentiment polarities, and the association between them. However, there is little work available to do this effectively. In
Massive open online courses (MOOCs) are redefining the education system and transcending boundaries posed by traditional courses. With the increase in popularity of online courses, there is a corresponding increase in the need to understand and interpret the communications of the
Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term (aspect), sentiment and opinion (opinion) triplets from sentences can tell a complete story, i.e., the discussed aspect, toward cause of sentiment. ASTE is charming task, however, one triplet extracted by only
Aspect sentiment triplet extraction (ASTE), which aims to identify aspects from review sentences along with their corresponding opinion expressions and sentiments, is an emerging task in fine-grained mining. Since ASTE consists of multiple subtasks, including entity extraction, r