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Browsing by Author "Batista, Fernando"

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  • Early experiments on automatic annotation of Portuguese medieval texts
    Publication . Bico, Maria Inês; Baptista, Jorge; Batista, Fernando; Cardeira, Esperança
    This paper presents the challenges and solutions adopted to the lemmatization and part-of-speech (PoS) tagging of a corpus of Old Portuguese texts (up to 1525), to pave the way to the implementation of an automatic annotation of these Medieval texts. A highly granular tagset, previously devised for Modern Portuguese, was adapted to this end. A large text (similar to 155 thousand words) was manually annotated for PoS and lemmata and used to train an initial PoS-tagger model. When applied to two other texts, the resulting model attained 91.2% precision with a textual variant of the same text, and 67.4% with a new, unseen text. A second model was then trained with the data provided by the previous three texts and applied to two other unseen texts. The new model achieved a precision of 77.3% and 82.4%, respectively.
    2022Journal article Open Access Show more
  • Examining Airbnb guest satisfaction tendencies: a text mining approach
    Publication . Cavique, Mariana; Ribeiro, Ricardo; Batista, Fernando; Correia, Antónia
    Given Airbnb's changes since its inception and the dynamism of customer preferences, a study that sheds light on how customer satisfaction is evolving is relevant. An automated method is proposed for identifying these satisfaction tendencies at a large scale. This study follows a text mining approach to analyse 590,070 reviews posted between 2010 and 2019 on the Airbnb platform in Lisbon. Topic Modelling is employed in order to identify the main topics discussed in the reviews, and Sentiment Analysis to understand the topics that compose guest's satisfaction in the context of Airbnb services. Three major topics are extracted from Airbnb reviews: 'host's service', 'physical aspects', and 'location'. Although a positivity bias in guest reviews is confirmed, the satisfaction level seems to be decreasing over the years. The results also reveal that 'physical aspects' is the predominant topic when considering the negative guest reviews. This research considers big data the base to create knowledge, data spanning over the years, offering consistency to the research.
    2022Journal article Restricted Show more