Automatic Classification of Railway Complaints using Machine Learning
People may now express their thoughts and ideas with a wider audience because of the popularity of social media sites like Twitter, Instagram, and Facebook. Businesses now utilise Twitter to reply to client comments, reviews, and grievances. Every day, millions of individuals discuss a wide range of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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/2024/07/e3sconf_star2024_00085.pdf |
| _version_ | 1797343467328765952 |
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| author | Sathivika Roy Tulasi Vasukidevi G. Malleswari T.Y.J. Naga Ushasukhanya S. Namratha Nayani |
| author_facet | Sathivika Roy Tulasi Vasukidevi G. Malleswari T.Y.J. Naga Ushasukhanya S. Namratha Nayani |
| author_sort | Sathivika Roy Tulasi |
| collection | DOAJ |
| description | People may now express their thoughts and ideas with a wider audience because of the popularity of social media sites like Twitter, Instagram, and Facebook. Businesses now utilise Twitter to reply to client comments, reviews, and grievances. Every day, millions of individuals discuss a wide range of issues on Twitter by sharing their ideas and interests. Sentiment analysis is a useful method for analysing such data, which involves identifying the sentiment of the source text and classifying it as positive, neutral, or negative. However, due to the vast amount of data, it can be challenging for businesses to address every customer’s question or complaint in a timely manner. Some issues may be urgent but delayed due to the volume of information. In order to prioritize emergency tweets, a system is proposed that utilizes machine learning algorithms such as Random Forest, Support Vector Machine, Logistic Regression, and Naïve Bayes to identify tweets based on their urgency. The proposed system gathers and preprocesses unstructured data, performs feature extraction, trains, assesses and compares multiple machine learning models to determine the best classifier with the highest accuracy, and uses vectorization via a pipeline to determine the sentiment of a new tweet provided as input. |
| first_indexed | 2024-03-08T10:48:07Z |
| format | Article |
| id | doaj.art-378a9d74009147708445e20679fbd7b8 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T10:48:07Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-378a9d74009147708445e20679fbd7b82024-01-26T16:52:14ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014770008510.1051/e3sconf/202447700085e3sconf_star2024_00085Automatic Classification of Railway Complaints using Machine LearningSathivika Roy Tulasi0Vasukidevi G.1Malleswari T.Y.J. Naga2Ushasukhanya S.3Namratha Nayani4Department of Networking and Communications, SRM Institute of Science and TechnologyDepartment of Networking and Communications, SRM Institute of Science and TechnologyDepartment of Networking and Communications, SRM Institute of Science and TechnologyDepartment of Networking and Communications, SRM Institute of Science and TechnologyDepartment of Networking and Communications, SRM Institute of Science and TechnologyPeople may now express their thoughts and ideas with a wider audience because of the popularity of social media sites like Twitter, Instagram, and Facebook. Businesses now utilise Twitter to reply to client comments, reviews, and grievances. Every day, millions of individuals discuss a wide range of issues on Twitter by sharing their ideas and interests. Sentiment analysis is a useful method for analysing such data, which involves identifying the sentiment of the source text and classifying it as positive, neutral, or negative. However, due to the vast amount of data, it can be challenging for businesses to address every customer’s question or complaint in a timely manner. Some issues may be urgent but delayed due to the volume of information. In order to prioritize emergency tweets, a system is proposed that utilizes machine learning algorithms such as Random Forest, Support Vector Machine, Logistic Regression, and Naïve Bayes to identify tweets based on their urgency. The proposed system gathers and preprocesses unstructured data, performs feature extraction, trains, assesses and compares multiple machine learning models to determine the best classifier with the highest accuracy, and uses vectorization via a pipeline to determine the sentiment of a new tweet provided as input.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/07/e3sconf_star2024_00085.pdfmachine learningrandom forest classifiersupport vector machinelogistic regressionnaïve bayes classifiertwitter api |
| spellingShingle | Sathivika Roy Tulasi Vasukidevi G. Malleswari T.Y.J. Naga Ushasukhanya S. Namratha Nayani Automatic Classification of Railway Complaints using Machine Learning E3S Web of Conferences machine learning random forest classifier support vector machine logistic regression naïve bayes classifier twitter api |
| title | Automatic Classification of Railway Complaints using Machine Learning |
| title_full | Automatic Classification of Railway Complaints using Machine Learning |
| title_fullStr | Automatic Classification of Railway Complaints using Machine Learning |
| title_full_unstemmed | Automatic Classification of Railway Complaints using Machine Learning |
| title_short | Automatic Classification of Railway Complaints using Machine Learning |
| title_sort | automatic classification of railway complaints using machine learning |
| topic | machine learning random forest classifier support vector machine logistic regression naïve bayes classifier twitter api |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/07/e3sconf_star2024_00085.pdf |
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