Next Article in Journal
Estimating Weibull Parameters Using Mabchour’s Method (MMab) for Wind Power at RAWA City, Iraq
Previous Article in Journal
Impacts of COVID-19 on Electric Vehicle Charging Behavior: Data Analytics, Visualization, and Clustering
Previous Article in Special Issue
A Comparative Analysis of Active Learning for Biomedical Text Mining
 
 
Article

A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM

School of Computer Science and Engineering, Central South University, Changsha 410083, China
*
Author to whom correspondence should be addressed.
Academic Editors: Patrícia Ramos and José Oliveira
Appl. Syst. Innov. 2022, 5(1), 13; https://doi.org/10.3390/asi5010013
Received: 28 November 2021 / Revised: 6 January 2022 / Accepted: 11 January 2022 / Published: 14 January 2022
(This article belongs to the Special Issue Advanced Machine Learning Techniques, Applications and Developments)
Twitter sentiment detectors (TSDs) provide a better solution to evaluate the quality of service and product than other traditional technologies. The classification accuracy and detection performance of TSDs, which are extremely reliant on the performance of the classification techniques, are used, and the quality of input features is provided. However, the time required is a big problem for the existing machine learning methods, which leads to a challenge for all enterprises that aim to transform their businesses to be processed by automated workflows. Deep learning techniques have been utilized in several real-world applications in different fields such as sentiment analysis. Deep learning approaches use different algorithms to obtain information from raw data such as texts or tweets and represent them in certain types of models. These models are used to infer information about new datasets that have not been modeled yet. We present a new effective method of sentiment analysis using deep learning architectures by combining the “universal language model fine-tuning” (ULMFiT) with support vector machine (SVM) to increase the detection efficiency and accuracy. The method introduces a new deep learning approach for Twitter sentiment analysis to detect the attitudes of people toward certain products based on their comments. The extensive results on three datasets illustrate that our model achieves the state-of-the-art results over all datasets. For example, the accuracy performance is 99.78% when it is applied on the Twitter US Airlines dataset. View Full-Text
Keywords: machine learning; transfer learning; sentiment analysis; SVM; ULMFiT; US airlines machine learning; transfer learning; sentiment analysis; SVM; ULMFiT; US airlines
Show Figures

Figure 1

MDPI and ACS Style

AlBadani, B.; Shi, R.; Dong, J. A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM. Appl. Syst. Innov. 2022, 5, 13. https://doi.org/10.3390/asi5010013

AMA Style

AlBadani B, Shi R, Dong J. A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM. Applied System Innovation. 2022; 5(1):13. https://doi.org/10.3390/asi5010013

Chicago/Turabian Style

AlBadani, Barakat, Ronghua Shi, and Jian Dong. 2022. "A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM" Applied System Innovation 5, no. 1: 13. https://doi.org/10.3390/asi5010013

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