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Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data

Author

Listed:
  • Nasir Jamal

    (School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China)

  • Chen Xianqiao

    (School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China)

  • Hamza Aldabbas

    (Prince Abdullah bin Ghazi Faculty of Information and Technology, Al-Balqa Applied University, Al Salt 19117, Jordan)

Abstract

Emotions detection in social media is very effective to measure the mood of people about a specific topic, news, or product. It has a wide range of applications, including identifying psychological conditions such as anxiety or depression in users. However, it is a challenging task to distinguish useful emotions’ features from a large corpus of text because emotions are subjective, with limited fuzzy boundaries that may be expressed in different terminologies and perceptions. To tackle this issue, this paper presents a hybrid approach of deep learning based on TensorFlow with Keras for emotions detection on a large scale of imbalanced tweets’ data. First, preprocessing steps are used to get useful features from raw tweets without noisy data. Second, the entropy weighting method is used to compute the importance of each feature. Third, class balancer is applied to balance each class. Fourth, Principal Component Analysis (PCA) is applied to transform high correlated features into normalized forms. Finally, the TensorFlow based deep learning with Keras algorithm is proposed to predict high-quality features for emotions classification. The proposed methodology is analyzed on a dataset of 1,600,000 tweets collected from the website ‘kaggle’. Comparison is made of the proposed approach with other state of the art techniques on different training ratios. It is proved that the proposed approach outperformed among other techniques.

Suggested Citation

  • Nasir Jamal & Chen Xianqiao & Hamza Aldabbas, 2019. "Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data," Future Internet, MDPI, vol. 11(9), pages 1-13, September.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:9:p:190-:d:263379
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    References listed on IDEAS

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    1. Kaplan, Andreas M. & Haenlein, Michael, 2010. "Users of the world, unite! The challenges and opportunities of Social Media," Business Horizons, Elsevier, vol. 53(1), pages 59-68, January.
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