The University of Southampton
University of Southampton Institutional Repository

A deep learning-based sentiment analysis approach for online product ranking With probabilistic linguistic term sets

A deep learning-based sentiment analysis approach for online product ranking With probabilistic linguistic term sets
A deep learning-based sentiment analysis approach for online product ranking With probabilistic linguistic term sets
The probabilities linguistic term set (PLTS) is an efficient tool to represent sentimental intensities hidden in unstructured text reviews that are useful for multicriteria online product ranking. Traditional machine learning-based sentiment analysis methods adopted in existing studies to obtain PLTSs often result in unsatisfying prediction accuracy and, thus, inevitably affect product ranking results. To overcome this limitation, in this study, we propose a deep learning-based sentiment analysis approach to produce PLTSs from online product reviews to rank online products. A natural language processing-based method is first applied to extract product features and corresponding feature texts from online reviews. Then, state-of-the-art deep learning-based models are implemented to conduct the sentiment classification for online product/feature review texts. To ensure classification accuracy, we propose an experimental matching mechanism to identify the level of sentiment tendency for all rating labels of a review dataset and then match each label with the most appropriate linguistic term. The experimental results reveal that our matching mechanism can benefit the training of a text classification model to identify sentiment tendencies from review texts with high prediction accuracy and with the help of the trained classification model, our approach can predict sentimental intensities of the extracted features' texts in the form of PLTSs with competitive accuracy. A case study of applying PLTSs output from our approach to an online product decision-making problem is also provided to validate the applicability of our approach.
deep learning, probabilistic linguistic term set (PLTS), sentiment analysis, text classification, text reviews, Sentiment analysis, Probabilistic logic, Deep learning (DL), Support vector machines, Training, Hidden Markov models, Linguistics, Feature extraction
0018-9391
Liu, Zixu
1b07df56-a07b-4e78-bebc-38657ca80f76
Liao, Huchang
6291d232-5410-49c5-8ed9-6eb4f9924599
Li, Maolin
47c06b6c-2001-442a-9500-29fcf5ce2199
Yang, Qian
e70e30bf-9615-435c-88af-e25c948bd97f
Meng, Fanlin
4aac4ca7-c2cd-4dd9-8051-b90f1bac9d3a
Liu, Zixu
1b07df56-a07b-4e78-bebc-38657ca80f76
Liao, Huchang
6291d232-5410-49c5-8ed9-6eb4f9924599
Li, Maolin
47c06b6c-2001-442a-9500-29fcf5ce2199
Yang, Qian
e70e30bf-9615-435c-88af-e25c948bd97f
Meng, Fanlin
4aac4ca7-c2cd-4dd9-8051-b90f1bac9d3a

Liu, Zixu, Liao, Huchang, Li, Maolin, Yang, Qian and Meng, Fanlin (2023) A deep learning-based sentiment analysis approach for online product ranking With probabilistic linguistic term sets. IEEE Transactions on Engineering Management. (doi:10.1109/TEM.2023.3271597).

Record type: Article

Abstract

The probabilities linguistic term set (PLTS) is an efficient tool to represent sentimental intensities hidden in unstructured text reviews that are useful for multicriteria online product ranking. Traditional machine learning-based sentiment analysis methods adopted in existing studies to obtain PLTSs often result in unsatisfying prediction accuracy and, thus, inevitably affect product ranking results. To overcome this limitation, in this study, we propose a deep learning-based sentiment analysis approach to produce PLTSs from online product reviews to rank online products. A natural language processing-based method is first applied to extract product features and corresponding feature texts from online reviews. Then, state-of-the-art deep learning-based models are implemented to conduct the sentiment classification for online product/feature review texts. To ensure classification accuracy, we propose an experimental matching mechanism to identify the level of sentiment tendency for all rating labels of a review dataset and then match each label with the most appropriate linguistic term. The experimental results reveal that our matching mechanism can benefit the training of a text classification model to identify sentiment tendencies from review texts with high prediction accuracy and with the help of the trained classification model, our approach can predict sentimental intensities of the extracted features' texts in the form of PLTSs with competitive accuracy. A case study of applying PLTSs output from our approach to an online product decision-making problem is also provided to validate the applicability of our approach.

Text
TEM final version liu pdf version - Accepted Manuscript
Download (932kB)

More information

Accepted/In Press date: 25 April 2023
e-pub ahead of print date: 15 May 2023
Published date: 15 May 2023
Additional Information: Publisher Copyright: IEEE
Keywords: deep learning, probabilistic linguistic term set (PLTS), sentiment analysis, text classification, text reviews, Sentiment analysis, Probabilistic logic, Deep learning (DL), Support vector machines, Training, Hidden Markov models, Linguistics, Feature extraction

Identifiers

Local EPrints ID: 476928
URI: http://eprints.soton.ac.uk/id/eprint/476928
ISSN: 0018-9391
PURE UUID: f863dd47-0e5d-414c-aa47-b1c1d66b3b9d
ORCID for Zixu Liu: ORCID iD orcid.org/0000-0002-4806-5482

Catalogue record

Date deposited: 19 May 2023 16:44
Last modified: 17 Mar 2024 04:15

Export record

Altmetrics

Contributors

Author: Zixu Liu ORCID iD
Author: Huchang Liao
Author: Maolin Li
Author: Qian Yang
Author: Fanlin Meng

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×