research-article

Leveraging aspect-based sentiment prediction with textual features and document metadata

Authors:

Konstantinos Korovesis,

Georgios Alexandridis,

George Caridakis,

Pavlos Polydoras,

Panagiotis TsantilasAuthors Info & Claims

SETN 2020: 11th Hellenic Conference on Artificial Intelligence

Pages 168 - 174

https://doi.org/10.1145/3411408.3411433

Published: 02 September 2020 Publication History

Abstract

Aspect-based sentiment prediction is a specific area of sentiment analysis that models the sentiment of a text excerpt as a multi-dimensional quantity pertaining to various interpretations, rather than a scalar one, that admits a single explanation. Extending earlier work, the said task is examined as a part of a unified architecture that collects, analyzes and stores documents from various online sources, including blogs & social network posts. The obtained data are processed at various levels; initially, a hybrid, attention-based bi-directional long short-term memory network, coupled with convolutional layers, is used to extract the textual features of the document. Following, an additional number of document metadata are also examined, such as the number of repetitions, the existence, type and frequency of emoji ideograms and, especially, the presence of keywords, assigned either manually (e.g. in the form of hashtags) or automatically. All of the aforementioned features are subsequently provided as input to a fully-connected, multi-layered, feed-forward artificial neural network that performs the final prediction task. The overall approach is tested on a large corpus of documents, with encouraging results.

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Cited By

Aivatoglou GFytili AArampatzis GZaikis DStylianou NVlahavas I(2024)End-to-End Aspect Extraction and Aspect-Based Sentiment Analysis Framework for Low-Resource LanguagesIntelligent Systems and Applications10.1007/978-3-031-47715-7_56(841-858)Online publication date: 30-Jan-2024
https://doi.org/10.1007/978-3-031-47715-7_56
Ahmed TIvan SKabir MMahmud HHasan K(2022)Performance analysis of transformer-based architectures and their ensembles to detect trait-based cyberbullyingSocial Network Analysis and Mining10.1007/s13278-022-00934-412:1Online publication date: 2-Aug-2022
https://doi.org/10.1007/s13278-022-00934-4
Fang YYang SZhao BHuang C(2021)Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention MechanismInformation10.3390/info1204017112:4(171)Online publication date: 16-Apr-2021
https://doi.org/10.3390/info12040171
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Leveraging aspect-based sentiment prediction with textual features and document metadata
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
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cover image ACM Other conferences

SETN 2020: 11th Hellenic Conference on Artificial Intelligence

September 2020

249 pages

ISBN:9781450388788

DOI:10.1145/3411408

Copyright © 2020 ACM.

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Published: 02 September 2020

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SETN 2020: 11th Hellenic Conference on Artificial Intelligence

September 2 - 4, 2020

Athens, Greece

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Cited By

Aivatoglou GFytili AArampatzis GZaikis DStylianou NVlahavas I(2024)End-to-End Aspect Extraction and Aspect-Based Sentiment Analysis Framework for Low-Resource LanguagesIntelligent Systems and Applications10.1007/978-3-031-47715-7_56(841-858)Online publication date: 30-Jan-2024
https://doi.org/10.1007/978-3-031-47715-7_56
Ahmed TIvan SKabir MMahmud HHasan K(2022)Performance analysis of transformer-based architectures and their ensembles to detect trait-based cyberbullyingSocial Network Analysis and Mining10.1007/s13278-022-00934-412:1Online publication date: 2-Aug-2022
https://doi.org/10.1007/s13278-022-00934-4
Fang YYang SZhao BHuang C(2021)Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention MechanismInformation10.3390/info1204017112:4(171)Online publication date: 16-Apr-2021
https://doi.org/10.3390/info12040171
Lye STeh P(2021)Customer Intent Prediction using Sentiment Analysis Techniques2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)10.1109/IDAACS53288.2021.9660391(185-190)Online publication date: 22-Sep-2021
https://dl.acm.org/doi/10.1109/IDAACS53288.2021.9660391

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