RETRACTED: Sentiment analysis in aspect term extraction for mobile phone tweets using machine learning techniques
International Journal of Pervasive Computing and Communications
ISSN: 1742-7371
Article publication date: 18 October 2021
Issue publication date: 8 November 2024
Retraction statement
The publishers of the International Journal of Pervasive Computing and Communications wish to retract the article Naramula, V. and A., K. (2021), “Sentiment analysis in aspect term extraction for mobile phone tweets using machine learning techniques”, International Journal of Pervasive Computing and Communications, Vol. 20 No. 4, pp. 1-20. https://doi.org/10.1108/IJPCC-06-2021-0143
An internal investigation into a series of submissions has uncovered evidence that the peer review process was compromised. As a result of these concerns, the findings of the article cannot be relied upon. This decision has been taken in accordance with Emerald's publishing ethics and the COPE guidelines on retractions.
The authors of this paper would like to note that they do not agree with the content of this notice.
Abstract
Purpose
This paper aims to focus on extracting aspect terms on mobile phone (iPhone and Samsung) tweets using NLTK techniques on multiple aspect extraction is one of the challenges. Then, also machine learning techniques are used that can be trained on supervised strategies to predict and classify sentiment present in mobile phone tweets. This paper also presents the proposed architecture for the extraction of aspect terms and sentiment polarity from customer tweets.
Design/methodology/approach
In the aspect-based sentiment analysis aspect, term extraction is one of the key challenges where different aspects are extracted from online user-generated content. This study focuses on customer tweets/reviews on different mobile products which is an important form of opinionated content by looking at different aspects. Different deep learning techniques are used to extract all aspects from customer tweets which are extracted using Twitter API.
Findings
The comparison of the results with traditional machine learning methods such as random forest algorithm, K-nearest neighbour and support vector machine using two data sets iPhone tweets and Samsung tweets have been presented for better accuracy.
Originality/value
In this paper, the authors have focused on extracting aspect terms on mobile phone (iPhone and Samsung) tweets using NLTK techniques on multi-aspect extraction is one of the challenges. Then, also machine learning techniques are used that can be trained on supervised strategies to predict and classify sentiment present in mobile phone tweets. This paper also presents the proposed architecture for the extraction of aspect terms and sentiment polarity from customer tweets.
Keywords
Citation
Naramula, V. and A., K. (2024), "RETRACTED: Sentiment analysis in aspect term extraction for mobile phone tweets using machine learning techniques", International Journal of Pervasive Computing and Communications, Vol. 20 No. 4, pp. 1-20. https://doi.org/10.1108/IJPCC-06-2021-0143
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited