David Vilares

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We describe an opinion mining system which classifies the polarity of Spanish texts. We propose an NLP approach that undertakes pre-processing, tokenisation and POS tagging of texts to then obtain the syntactic structure of sentences by means of a dependency parser. This structure is then used to address three of the most significant linguistic(More)
This paper describes our participation at the third edition of the workshop on Sentiment Analysis focused on Spanish tweets, tass 2014. This year’s evaluation campaign includes four challenges: (1) global sentiment analysis, (2) topic classification, (3) aspect-extraction and (4) aspect-based sentiment analysis. Tasks 1 and 2 are addressed from a machine(More)
This paper describes our participation at RepLab 2014, a competitive evaluation for reputation monitoring on Twitter. The following tasks were addressed: (1) categorisation of tweets with respect to standard reputation dimensions and (2) characterisation of Twitter profiles, which includes: (2.1) identifying the type of those profiles, such as journalist or(More)
Millions of micro texts are published every day on Twitter. Identifying the sentiment present in them can be helpful for measuring the frame of mind of the public, their satisfaction with respect to a product or their support of a social event. In this context, polarity classification is a subfield of sentiment analysis focussed on determining whether the(More)
We describe a system that classifies the polarity of Spanish tweets. We adopt a hybrid approach, which combines machine learning and linguistic knowledge acquired by means of NLP. We use part-of-speech tags, syntactic dependencies and semantic knowledge as features for a supervised classifier. Lexical particularities of the language used in Twitter are(More)
We introduce an approach to train lexicalized parsers using bilingual corpora obtained by merging harmonized treebanks of different languages, producing parsers that can analyze sentences in either of the learned languages, or even sentences that mix both. We test the approach on the Universal Dependency Treebanks, training with MaltParser and(More)
This paper describes the participation of the LyS group at tass 2015. In this year’s edition, we used a long short-term memory neural network to address the two proposed challenges: (1) sentiment analysis at a global level and (2) aspect-based sentiment analysis on football and political tweets. The performance of this deep learning approach is compared to(More)
This work describes the system for the normalization of tweets in Spanish designed by the Language in the Information Society (LYS) Group of the University of A Coruña for Tweet-Norm 2013. It is a conceptually simple and flexible system, which uses few resources and that faces the problem from a lexical point of view.
This article describes the approach developed by our group in order to resolve the sentiment analysis at a global level, topic identification and political tendency classification tasks on Spanish tweets; proposed at the Workshop of Sentiment Analysis at sepln (tass 2013). As a preliminary step, we carry out an ad-hoc preprocessing in order to normalise the(More)
This article describes an opinion mining system that classifies the polarity of Spanish texts. We propose a nlp-based approach which performs segmentation, tokenization and pos tagging of texts to then obtain the syntactic structure of sentences by means of a dependency parser. The syntactic structure is then used to address three of the most significant(More)