Sentiment analysis of Twitter texts using Machine learning algorithms

Hawar BARZENJI

doi:https://doi.org/10.21541/apjes.939338

Makale detayları

Sentiment analysis of Twitter texts using Machine learning algorithms

Yazarlar:

Hawar BARZENJI

Yayıncı:

Akademik Perspektif Derneği

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Academic Platform - Journal of Engineering and Science 2021 Vol.9 Issue 3, pp.460-471

Belge Tipi:

Makale

Yayın Tarihi:

9/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000874392

Sıklık:

Aylık

eISSN:

2822-2385

DOI:

https://doi.org/10.21541/apjes.939338

Özet:

 

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.

Makale detayları

Sentiment analysis of Twitter texts using Machine learning algorithms

Yazarlar: Hawar BARZENJI

Yayıncı: Akademik Perspektif Derneği

Ana Başlıklar: Uygulamalı fizik

Dergi: Academic Platform - Journal of Engineering and Science 2021 Vol.9 Issue 3, pp.460-471

Belge Tipi: Makale

Yayın Tarihi: 9/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000874392

Sıklık: Aylık

eISSN: 2822-2385

DOI: https://doi.org/10.21541/apjes.939338

Sayı

Yazarlar:

Hawar BARZENJI

Yayıncı:

Akademik Perspektif Derneği

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Academic Platform - Journal of Engineering and Science 2021 Vol.9 Issue 3, pp.460-471

Belge Tipi:

Makale

Yayın Tarihi:

9/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000874392

Sıklık:

Aylık

eISSN:

2822-2385

DOI:

https://doi.org/10.21541/apjes.939338