Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques
The advancements in social networking have empowered open expression on micro-blogging platforms like Twitter. Traditional Twitter Sentiment Analysis (TSA) faces challenges due to rule-based or dictionary algorithms, dealing with feature selection, ambiguity, sparse data, and language variations. Th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/11/486 |
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| author | Manjog Padhy Umar Muhammad Modibbo Rasmita Rautray Subhranshu Sekhar Tripathy Sujit Bebortta |
| author_facet | Manjog Padhy Umar Muhammad Modibbo Rasmita Rautray Subhranshu Sekhar Tripathy Sujit Bebortta |
| author_sort | Manjog Padhy |
| collection | DOAJ |
| description | The advancements in social networking have empowered open expression on micro-blogging platforms like Twitter. Traditional Twitter Sentiment Analysis (TSA) faces challenges due to rule-based or dictionary algorithms, dealing with feature selection, ambiguity, sparse data, and language variations. This study proposed a classification framework for Twitter sentiment data using word count vectorization and machine learning techniques to reduce the difficulties faced with annotated sentiment-labelled tweets. Various classifiers (Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF)) were evaluated based on accuracy, precision, recall, F1-score, and specificity. Random Forest outperformed the others with an Area under Curve (AUC) value of 0.96 and an average precision (AP) score of 0.96 in sentiment classification, especially effective with minimal Twitter-specific features. |
| format | Article |
| id | doaj-art-41efd91c3d9a4700b6d1e1464f06baac |
| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-41efd91c3d9a4700b6d1e1464f06baac2025-08-20T02:26:45ZengMDPI AGAlgorithms1999-48932024-10-01171148610.3390/a17110486Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization TechniquesManjog Padhy0Umar Muhammad Modibbo1Rasmita Rautray2Subhranshu Sekhar Tripathy3Sujit Bebortta4Department of Computer Science and Engineering, Siksha‘O’Anusandhan University, Bhubaneswar 751030, Odisha, IndiaDepartment of Operations Research, Modibbo Adama University, Yola PMB 2076, NigeriaDepartment of Computer Science and Engineering, Siksha‘O’Anusandhan University, Bhubaneswar 751030, Odisha, IndiaSchool of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, IndiaDepartment of Computer Science, Ravenshaw University, Cuttack 753003, Odisha, IndiaThe advancements in social networking have empowered open expression on micro-blogging platforms like Twitter. Traditional Twitter Sentiment Analysis (TSA) faces challenges due to rule-based or dictionary algorithms, dealing with feature selection, ambiguity, sparse data, and language variations. This study proposed a classification framework for Twitter sentiment data using word count vectorization and machine learning techniques to reduce the difficulties faced with annotated sentiment-labelled tweets. Various classifiers (Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF)) were evaluated based on accuracy, precision, recall, F1-score, and specificity. Random Forest outperformed the others with an Area under Curve (AUC) value of 0.96 and an average precision (AP) score of 0.96 in sentiment classification, especially effective with minimal Twitter-specific features.https://www.mdpi.com/1999-4893/17/11/486sentiment classificationTwitter sentiment analysisword count vectorizationmachine learning |
| spellingShingle | Manjog Padhy Umar Muhammad Modibbo Rasmita Rautray Subhranshu Sekhar Tripathy Sujit Bebortta Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques Algorithms sentiment classification Twitter sentiment analysis word count vectorization machine learning |
| title | Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques |
| title_full | Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques |
| title_fullStr | Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques |
| title_full_unstemmed | Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques |
| title_short | Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques |
| title_sort | application of machine learning techniques to classify twitter sentiments using vectorization techniques |
| topic | sentiment classification Twitter sentiment analysis word count vectorization machine learning |
| url | https://www.mdpi.com/1999-4893/17/11/486 |
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