@inproceedings{vasconcelos-etal-2020-aspect,
    title = "Aspect Flow Representation and Audio Inspired Analysis for Texts",
    author = "Vasconcelos, Larissa  and
      Campelo, Claudio  and
      Jeronimo, Caio",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.lrec-1.183/",
    pages = "1469--1477",
    language = "eng",
    ISBN = "979-10-95546-34-4",
    abstract = "For better understanding how people write texts, it is fundamental to examine how a particular aspect (e.g., subjectivity, sentiment, argumentation) is exploited in a text. Analysing such an aspect of a text as a whole (i.e., through a summarised single feature) can lead to significant information loss. In this paper, we propose a novel method of representing and analysing texts that consider how an aspect behaves throughout the text. We represent the texts by aspect flows for capturing all the aspect behaviour. Then, inspired by the resemblance between these flows format and a sound waveform, we fragment them into frames and calculate an adaptation of audio analysis features, named here Audio-Like Features, as a way of analysing the texts. The results of the conducted classification tasks reveal that our approach can surpass methods based on summarised features. We also show that a detailed examination of the Audio-Like Features can lead to a more profound knowledge about the represented texts."
}