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Tweets Classification on the Base of Sentiments for US Airline Companies

Entropy (Basel, Switzerland), 2019-11, Vol.21 (11), p.1078 [Peer Reviewed Journal]

2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2019 by the authors. 2019 ;ISSN: 1099-4300 ;EISSN: 1099-4300 ;DOI: 10.3390/e21111078

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  • Title:
    Tweets Classification on the Base of Sentiments for US Airline Companies
  • Author: Rustam, Furqan ; Ashraf, Imran ; Mehmood, Arif ; Ullah, Saleem ; Choi, Gyu
  • Subjects: Airlines ; Classification ; Classifiers ; Data mining ; Datasets ; ensemble classifier ; Feature extraction ; long short-term memory network ; Machine learning ; Organizations ; Performance measurement ; Sentiment analysis ; Social networks ; supervised machine learning ; text classification ; text mining ; Voting
  • Is Part Of: Entropy (Basel, Switzerland), 2019-11, Vol.21 (11), p.1078
  • Description: The use of data from social networks such as Twitter has been increased during the last few years to improve political campaigns, quality of products and services, sentiment analysis, etc. Tweets classification based on user sentiments is a collaborative and important task for many organizations. This paper proposes a voting classifier (VC) to help sentiment analysis for such organizations. The VC is based on logistic regression (LR) and stochastic gradient descent classifier (SGDC) and uses a soft voting mechanism to make the final prediction. Tweets were classified into positive, negative and neutral classes based on the sentiments they contain. In addition, a variety of machine learning classifiers were evaluated using accuracy, precision, recall and F1 score as the performance metrics. The impact of feature extraction techniques, including term frequency (TF), term frequency-inverse document frequency (TF-IDF), and word2vec, on classification accuracy was investigated as well. Moreover, the performance of a deep long short-term memory (LSTM) network was analyzed on the selected dataset. The results show that the proposed VC performs better than that of other classifiers. The VC is able to achieve an accuracy of 0.789, and 0.791 with TF and TF-IDF feature extraction, respectively. The results demonstrate that ensemble classifiers achieve higher accuracy than non-ensemble classifiers. Experiments further proved that the performance of machine learning classifiers is better when TF-IDF is used as the feature extraction method. Word2vec feature extraction performs worse than TF and TF-IDF feature extraction. The LSTM achieves a lower accuracy than machine learning classifiers.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1099-4300
    EISSN: 1099-4300
    DOI: 10.3390/e21111078
  • Source: Open Access: PubMed Central
    Geneva Foundation Free Medical Journals at publisher websites
    AUTh Library subscriptions: ProQuest Central
    ROAD
    DOAJ Directory of Open Access Journals
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