Aspect Flow Representation and Audio Inspired Analysis for Texts

Larissa Vasconcelos, Claudio Campelo, Caio Jeronimo

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.
Anthology ID:
2020.lrec-1.183
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1469–1477
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.183
DOI:
Bibkey:
Cite (ACL):
Larissa Vasconcelos, Claudio Campelo, and Caio Jeronimo. 2020. Aspect Flow Representation and Audio Inspired Analysis for Texts. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1469–1477, Marseille, France. European Language Resources Association.
Cite (Informal):
Aspect Flow Representation and Audio Inspired Analysis for Texts (Vasconcelos et al., LREC 2020)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2020.lrec-1.183.pdf
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