Enhancing Sentiment Analysis Accuracy by Optimizing Hyperparameters of SVM and Logistic Regression Models
The Analysis of Sentiments expressed on Twitter is a widely practiced application of Natural Language Processing (NLP) and Artificial Intelligence (AI). This process involves examining tweets to determine the emotional tone conveyed within the message. AI-based approaches are employed in Twitter sen...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/02/e3sconf_icregcsd2023_01017.pdf |
| _version_ | 1827372080808591360 |
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| author | Siri Yellu Afroz Suhail Usha Rani Rella |
| author_facet | Siri Yellu Afroz Suhail Usha Rani Rella |
| author_sort | Siri Yellu |
| collection | DOAJ |
| description | The Analysis of Sentiments expressed on Twitter is a widely practiced application of Natural Language Processing (NLP) and Artificial Intelligence (AI). This process involves examining tweets to determine the emotional tone conveyed within the message. AI-based approaches are employed in Twitter sentiment analysis, typically following these steps: Data Collection, Data Preprocessing, and Sentiment Analysis, where AI techniques like Support Vector Machines (SVM) and Logistic Regression are utilized to categorize tweets into positive, negative, or neutral sentiments. Twitter data is a valuable source of information, serving diverse purposes such as real-time updates, user feedback, brand monitoring, market research, digital marketing, and political analysis. The Twitter API (Application Programming Interface) provides developers with tools and functionalities to access and interact with Twitter data, including tweets, user profiles, and timelines, enabling a wide range of applications and services. However, Twitter sentiment analysis presents challenges such as handling sarcasm, irony, colloquial language, and coping with the sheer volume and rapid flow of Twitter data. Nevertheless, with effective preprocessing techniques and AI methods, Twitter sentiment analysis can yield valuable insights into public opinion on various topics. |
| first_indexed | 2024-03-08T10:51:16Z |
| format | Article |
| id | doaj.art-48afa90146574d9f80a51bb4ea498c2d |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T10:51:16Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-48afa90146574d9f80a51bb4ea498c2d2024-01-26T16:47:00ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014720101710.1051/e3sconf/202447201017e3sconf_icregcsd2023_01017Enhancing Sentiment Analysis Accuracy by Optimizing Hyperparameters of SVM and Logistic Regression ModelsSiri Yellu0Afroz Suhail1Usha Rani Rella2Student, CSE, CVR College of EngineeringAssociate Professor, CSE Department, CVR College of EngineeringProfessor, CSE (AI&ML) Department, CVR College of EngineeringThe Analysis of Sentiments expressed on Twitter is a widely practiced application of Natural Language Processing (NLP) and Artificial Intelligence (AI). This process involves examining tweets to determine the emotional tone conveyed within the message. AI-based approaches are employed in Twitter sentiment analysis, typically following these steps: Data Collection, Data Preprocessing, and Sentiment Analysis, where AI techniques like Support Vector Machines (SVM) and Logistic Regression are utilized to categorize tweets into positive, negative, or neutral sentiments. Twitter data is a valuable source of information, serving diverse purposes such as real-time updates, user feedback, brand monitoring, market research, digital marketing, and political analysis. The Twitter API (Application Programming Interface) provides developers with tools and functionalities to access and interact with Twitter data, including tweets, user profiles, and timelines, enabling a wide range of applications and services. However, Twitter sentiment analysis presents challenges such as handling sarcasm, irony, colloquial language, and coping with the sheer volume and rapid flow of Twitter data. Nevertheless, with effective preprocessing techniques and AI methods, Twitter sentiment analysis can yield valuable insights into public opinion on various topics.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/02/e3sconf_icregcsd2023_01017.pdftwitteraisvmnlp |
| spellingShingle | Siri Yellu Afroz Suhail Usha Rani Rella Enhancing Sentiment Analysis Accuracy by Optimizing Hyperparameters of SVM and Logistic Regression Models E3S Web of Conferences ai svm nlp |
| title | Enhancing Sentiment Analysis Accuracy by Optimizing Hyperparameters of SVM and Logistic Regression Models |
| title_full | Enhancing Sentiment Analysis Accuracy by Optimizing Hyperparameters of SVM and Logistic Regression Models |
| title_fullStr | Enhancing Sentiment Analysis Accuracy by Optimizing Hyperparameters of SVM and Logistic Regression Models |
| title_full_unstemmed | Enhancing Sentiment Analysis Accuracy by Optimizing Hyperparameters of SVM and Logistic Regression Models |
| title_short | Enhancing Sentiment Analysis Accuracy by Optimizing Hyperparameters of SVM and Logistic Regression Models |
| title_sort | enhancing sentiment analysis accuracy by optimizing hyperparameters of svm and logistic regression models |
| topic | twitter ai svm nlp |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/02/e3sconf_icregcsd2023_01017.pdf |
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