Salud María Jiménez-Zafra

SemEval-2016 task 5 : aspect based sentiment analysis

This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams.

Combining resources to improve unsupervised sentiment analysis at aspect-level

Every day more companies are interested in users’ opinions about their products or services. Also, every day there are more users that search for reviews on the web before purchasing a product. These users and companies are not satisfied with knowing the overall sentiment of a product, they want a finer knowledge of users’ opinions. Owing to this fact, more and more researchers are working on sentiment analysis at aspect-level. This paper describes an unsupervised approach for aspect-based sentiment analysis, which aims to identify the aspects of given target entities and the sentiment expressed for each aspect. We have evaluated several tasks, although perhaps the major novelty is in the classification of the aspects. We employ a lexicon-based method combining different linguistic resources and we conclude that the combination of several classifiers improves the classification significantly. In addition, a comparison with a supervised system is performed in order to determine the strengths and weakness of each of them.

Studying the Scope of Negation for Spanish Sentiment Analysis on Twitter

Polarity classification is a well-known Sentiment Analysis task. However, most research has been oriented towards developing supervised or unsupervised systems without paying much attention to certain linguistic phenomena such as negation. In this paper we focus on this specific issue in order to demonstrate that dealing with negation can improve the final system. Although we can find some studies of negation detection, most of them deal with English documents. On the contrary, our study is focused on the scope of negation in Spanish Sentiment Analysis. Thus, we have built an unsupervised polarity classification system based on integrating external knowledge. In order to evaluate the influence of negation we have implemented a specific module for negation detection by applying several rules. The system has been tested considering and without considering negation, using a corpus of tweets written in Spanish. The results obtained reveal that the treatment of negation can greatly improve the accuracy of the final system. Moreover, we have carried out a comprehensive statistical study in order to demonstrate our approach. To the best of our knowledge, this is the first work which statistically demonstrates that taking into account negation significantly improves the polarity classification of Spanish tweets.

SINAI: Voting System for Twitter Sentiment Analysis

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.

SFU ReviewSP-NEG: a Spanish corpus annotated with negation for sentiment analysis. A typology of negation patterns

AbstractIn this paper, we present SFU ReviewSP-NEG, the first Spanish corpus annotated with negation with a wide coverage freely available. We describe the methodology applied in the annotation of the corpus including the tagset, the linguistic criteria and the inter-annotator agreement tests. We also include a complete typology of negation patterns in Spanish. This typology has the advantage that it is easy to express in terms of a tagset for corpus annotation: the types are clearly defined, which avoids ambiguity in the annotation process, and they provide wide coverage (i.e. they resolved all the cases occurring in the corpus). We use the SFU ReviewSP as a base in order to make the annotations. The corpus consists of 400 reviews, 221,866 words and 9455 sentences, out of which 3022 sentences contain at least one negation structure.

Lexicon Adaptation for Spanish Emotion Mining

Emotion mining is an emerging task that is still at a first stage of research. Most of the existing works and resources focus on English, but there are other languages, such as Spanish, whose presence on the Internet is greater every day. In WASSA-2017 Shared Task on Emotion Intensity, it was found that the best systems included features from affect lexicons. This fact combined with the scarcity of resources in Spanish, led us to build a new Spanish lexicon that has been tested over the dataset released at SemEval 2018 Task 1. Moreover, it has been compared with the unique emotion intensity lexicon existing in Spanish, SEL lexicon, and it has shown the difficulty of the task and the importance of continuing working on the development of resources.

Relevance of the SFU Review SP -NEG corpus annotated with the scope of negation for supervised polarity classification in Spanish

Abstract Up to now, negation is a challenging problem in the context of Sentiment Analysis. The study of negation implies the correct identification of negation markers, the scope and the interpretation of how negation affects the words that are within it, that is, whether it modifies their meaning or not and if so, whether it reverses, reduces or increments their polarity value. In addition, if we are interested in managing reviews in languages other than English, the issue becomes even more problematic due to the lack of resources. The present work shows the validity of the SFU ReviewSP-NEG corpus, which we annotated at negation level, for training supervised polarity classification systems in Spanish. The assessment has involved the comparison of different supervised models. The results achieved show the validity of the corpus and allow us to state that the annotation of how negation affects the words that are within its scope is important. Therefore, we propose to add a new phase to tackle negation in polarity classification systems (phase iii): i) identification of negation cues, ii) determination of the scope of negation, iii) identification of how negation affects the words that are within its scope, and iv) polarity classification taking into account negation.

SINAI: Syntactic Approach for Aspect-Based Sentiment Analysis

This paper describes the participation of the SINAI research group in the task Aspect Based Sentiment Analysis of SemEval Workshop 2015 Edition. We propose a syntactic approach for identifying the words that modify each aspect, with the aim of classifying the sentiment expressed towards each attribute of an entity.

A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining

We present the Trip-MAML dataset, a Multi-Lingual dataset of hotel reviews that have been manually annotated at the sentence-level with Multi-Aspect sentiment labels. This dataset has been built as an extension of an existent English-only dataset, adding documents written in Italian and Spanish. We detail the dataset construction process, covering the data gathering, selection, and annotation. We present inter-annotator agreement figures and baseline experimental results, comparing the three languages. Trip-MAML is a multi-lingual dataset for aspect-oriented opinion mining that enables researchers (i) to face the problem on languages other than English and (ii) to the experiment the application of cross-lingual learning methods to the task.

SINAI: Voting System for Aspect Based Sentiment Analysis

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.