Next Article in Journal
My-AHA: Software Platform to Promote Active and Healthy Ageing
Next Article in Special Issue
The BioVisualSpeech Corpus of Words with Sibilants for Speech Therapy Games Development
Previous Article in Journal
Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix
Previous Article in Special Issue
Developing Amaia: A Conversational Agent for Helping Portuguese Entrepreneurs—An Extensive Exploration of Question-Matching Approaches for Portuguese
Article

Evaluating Richer Features and Varied Machine Learning Models for Subjectivity Classification of Book Review Sentences in Portuguese

Interinstitutional Center for Computational Linguistics (NILC), Institute of Mathematics and Computer Science, University of São Paulo, São Carlos/SP 13566-590, Brazil
*
Author to whom correspondence should be addressed.
Information 2020, 11(9), 437; https://doi.org/10.3390/info11090437
Received: 31 July 2020 / Revised: 7 September 2020 / Accepted: 8 September 2020 / Published: 11 September 2020
(This article belongs to the Special Issue Selected Papers from PROPOR 2020)
Texts published on social media have been a valuable source of information for companies and users, as the analysis of this data helps improving/selecting products and services of interest. Due to the huge amount of data, techniques for automatically analyzing user opinions are necessary. The research field that investigates these techniques is called sentiment analysis. This paper focuses specifically on the task of subjectivity classification, which aims to predict whether a text passage conveys an opinion. We report the study and comparison of machine learning methods of different paradigms to perform subjectivity classification of book review sentences in Portuguese, which have shown to be a challenging domain in the area. Specifically, we explore richer features for the task, using several lexical, centrality-based and discourse features. We show the contributions of the different feature sets and evidence that the combination of lexical, centrality-based and discourse features produce better results than any of the feature sets individually. Additionally, by analyzing the achieved results and the acquired knowledge by some symbolic machine learning methods, we show that some discourse relations may clearly signal subjectivity. Our corpus annotation also reveals some distinctive discourse structuring patterns for sentence subjectivity. View Full-Text
Keywords: subjectivity classification; feature sets; discourse structure; Portuguese language subjectivity classification; feature sets; discourse structure; Portuguese language
Show Figures

Figure 1

MDPI and ACS Style

Belisário, L.B.; Ferreira, L.G.; Pardo, T.A.S. Evaluating Richer Features and Varied Machine Learning Models for Subjectivity Classification of Book Review Sentences in Portuguese. Information 2020, 11, 437. https://doi.org/10.3390/info11090437

AMA Style

Belisário LB, Ferreira LG, Pardo TAS. Evaluating Richer Features and Varied Machine Learning Models for Subjectivity Classification of Book Review Sentences in Portuguese. Information. 2020; 11(9):437. https://doi.org/10.3390/info11090437

Chicago/Turabian Style

Belisário, Luana B., Luiz G. Ferreira, and Thiago A.S. Pardo 2020. "Evaluating Richer Features and Varied Machine Learning Models for Subjectivity Classification of Book Review Sentences in Portuguese" Information 11, no. 9: 437. https://doi.org/10.3390/info11090437

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop