Machine learning-based sentiment analysis of Twitter data
The paper analyzes the views of Twitter users on the COVID-19 corona virus pandemic based on machine learning algorithms. The role of sentiment analysis increased with the advent of the social network era and the rapid spread of microblogging applications and forums. Social networks are the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Information Technology Publishing House
2022-01-01
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| Series: | Problems of Information Society |
| Online Access: | https://jpis.az/uploads/article/en/2022_1/MACHINE_LEARNING-BASED_SENTIMENT_ANALYSIS_OF_TWITTER_DATA.pdf |
| _version_ | 1797262508387467264 |
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| author | Makrufa Hajirahimova Marziya Ismayilova |
| author_facet | Makrufa Hajirahimova Marziya Ismayilova |
| author_sort | Makrufa Hajirahimova |
| collection | DOAJ |
| description | The paper analyzes the views of Twitter users on the COVID-19 corona virus pandemic based on machine learning algorithms. The role of sentiment analysis increased with the advent of the social network era and the rapid spread of microblogging applications and forums. Social networks are the main sources for gathering information about users’ thoughts on various themes. People spend more time on social media to share their thoughts with others. One of the themes discussed on social networking platforms Twitter is the COVID-19 corona virus pandemic. In the paper, machine learning methods as Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN) are used to analyze the emotional “color” (positive, negative, and neutral) of tweets related to the COVID-19 corona virus pandemic. The experiments are conducted in Python programming using the scikit-learn library. A tweet database related to the COVID-19 corona virus pandemic from the Kaggle website is used for experiments. The RF classifier shows the highest performance in the experiments. |
| first_indexed | 2024-04-24T23:58:14Z |
| format | Article |
| id | doaj.art-4bda2807f68e409b88250d34fd1c3505 |
| institution | Directory Open Access Journal |
| issn | 2077-964X 2309-7566 |
| language | English |
| last_indexed | 2024-04-24T23:58:14Z |
| publishDate | 2022-01-01 |
| publisher | Information Technology Publishing House |
| record_format | Article |
| series | Problems of Information Society |
| spelling | doaj.art-4bda2807f68e409b88250d34fd1c35052024-03-14T10:44:52ZengInformation Technology Publishing HouseProblems of Information Society2077-964X2309-75662022-01-01131526010.25045/jpis.v13.i1.07Machine learning-based sentiment analysis of Twitter dataMakrufa Hajirahimovahttps://orcid.org/0000-0003-0786-5974Marziya Ismayilovahttps://orcid.org/0000-0002-3080-0952 The paper analyzes the views of Twitter users on the COVID-19 corona virus pandemic based on machine learning algorithms. The role of sentiment analysis increased with the advent of the social network era and the rapid spread of microblogging applications and forums. Social networks are the main sources for gathering information about users’ thoughts on various themes. People spend more time on social media to share their thoughts with others. One of the themes discussed on social networking platforms Twitter is the COVID-19 corona virus pandemic. In the paper, machine learning methods as Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN) are used to analyze the emotional “color” (positive, negative, and neutral) of tweets related to the COVID-19 corona virus pandemic. The experiments are conducted in Python programming using the scikit-learn library. A tweet database related to the COVID-19 corona virus pandemic from the Kaggle website is used for experiments. The RF classifier shows the highest performance in the experiments.https://jpis.az/uploads/article/en/2022_1/MACHINE_LEARNING-BASED_SENTIMENT_ANALYSIS_OF_TWITTER_DATA.pdf |
| spellingShingle | Makrufa Hajirahimova Marziya Ismayilova Machine learning-based sentiment analysis of Twitter data Problems of Information Society |
| title | Machine learning-based sentiment analysis of Twitter data |
| title_full | Machine learning-based sentiment analysis of Twitter data |
| title_fullStr | Machine learning-based sentiment analysis of Twitter data |
| title_full_unstemmed | Machine learning-based sentiment analysis of Twitter data |
| title_short | Machine learning-based sentiment analysis of Twitter data |
| title_sort | machine learning based sentiment analysis of twitter data |
| url | https://jpis.az/uploads/article/en/2022_1/MACHINE_LEARNING-BASED_SENTIMENT_ANALYSIS_OF_TWITTER_DATA.pdf |
| work_keys_str_mv | AT makrufahajirahimova machinelearningbasedsentimentanalysisoftwitterdata AT marziyaismayilova machinelearningbasedsentimentanalysisoftwitterdata |