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Dergiler / Academic Platform Journal of Engineering and Smart Systems / 2021 / Cilt: 9 - Sayı: 3

Sentiment analysis of Twitter texts using Machine learning algorithms

Hawar BARZENJI
Sayfa
460–471
Yayın Tarihi
2021-09-30
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

We reached 89% accuracy with Support Vector Machines, 88% accuracy with Random Forest, and 72% accuracy with Gaussian Naive Bayes classifier.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Since the two last decades, social media networks have become a part of our daily life.
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Temel Bulgular & Veri:Today, getting information from social media, tracking trends in social media, learning the feelings and emotions of people on social media is very essential.
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Sonuç & Katkı:We reached 89% accuracy with Support Vector Machines, 88% accuracy with Random Forest, and 72% accuracy with Gaussian Naive Bayes classifier.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Natural Language Processing · Machine Learning · Random Forest

Abstract

Since the two last decades, social media networks have become a part of our daily life. Today, getting information from social media, tracking trends in social media, learning the feelings and emotions of people on social media is very essential. In this study, sentiment analysis was performed on Twitter text to learn about the subjective polarities of the writings. The polarities are positive, negative, and neutral. At the first stage of the sentiment analysis, a public data set has been obtained. Secondly, natural language processing techniques have been applied to make the data ready for machine learning training procedures. Lastly, sentiment analysis is performed by using three different machine learning algorithms. We reached 89% accuracy with Support Vector Machines, 88% accuracy with Random Forest, and 72% accuracy with Gaussian Naive Bayes classifier.

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Bu Makaleye Atıf Yap

BARZENJI, H. (2021). Sentiment analysis of Twitter texts using Machine learning algorithms. Academic Platform Journal of Engineering and Smart Systems, Cilt: 9 - Sayı: 3, 460–471. https://ajindex.com/articles/730606

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Kaynakça (16)

  1. Duncombe, Constance. "The politics of Twitter: emotions and the power of social media." International Political Sociology 13.4 (2019): 409-429
  2. Akram, Waseem, and Rekesh Kumar. "A study on positive and negative effects of social media on society." International Journal of Computer Sciences and Engineering 5.10 (2017): 347-354
  3. Ajjoub, Carl, Thomas Walker, and Yunfei Zhao. "Social media posts and stock returns: The Trump factor." International Journal of Managerial Finance (2020)
  4. Social Blade Organization, “Twitter Stats Summary,” User Statistics for RealDonalTrump. https://socialblade.com/twitter/user/realdonaldtrump (accessed Dec. 7, 2020)
  5. Wells, Chris, et al. "Trump, Twitter, and news media responsiveness: A media systems approach." New Media & Society 22.4 (2020): 659-682
  6. Clarke, Isobelle, and Jack Grieve. "Stylistic variation on the Donald Trump Twitter account: A linguistic analysis of tweets posted between 2009 and 2018." PloS one 14.9 (2019): e0222062
  7. Yaqub, Ussama, et al. "Analysis of political discourse on twitter in the context of the 2016 US presidential elections." Government Information Quarterly 34.4 (2017): 613-626
  8. Kaggle Data science Company, “Datasets,” Datasets. https://www.kaggle.com/austinreese/trump-tweets (accessed Nov.7, 2020)
  9. Kam, Ho Tin. "Random decision forest." Proceedings of the 3rd International Conference on Document Analysis and Recognition. Vol. 1416. Montreal, Canada, August, 1995
  10. Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32. Cutler, Adele, D. Richard Cutler, and John R. Stevens. "Random forests." Ensemble machine learning. Springer, Boston, MA, 2012. 157-175
  11. Scikit-learn Software. https://scikit-learn.org/stable/modules/naive_bayes.html (accessed May 2, 2021
  12. Syafie, Lukman, et al. "Comparison of Artificial Neural Network and Gaussian Naïve Bayes in Recognition of Hand-Writing Number." 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT). IEEE, 2018
  13. Cortes, Corinna, and Vladimir Vapnik. "Support-vector networks." Machine learning 20.3 (1995): 273-297
  14. Tharwat, Alaa. "Classification assessment methods." Applied Computing and Informatics (2020)
  15. Kulkarni, Ajay, Deri Chong, and Feras A. Batarseh. "Foundations of data imbalance and solutions for a data democracy." Data Democracy. Academic Press, 2020. 83-106
  16. Elbagir, Shihab, and Jing Yang. "Twitter sentiment analysis using natural language tool