Comparative study on sentimental analysis using machine learning techniques
With the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is because users post the reply comments in social media...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Mehran University of Engineering and Technology
2023-01-01
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| Series: | Mehran University Research Journal of Engineering and Technology |
| Online Access: | https://publications.muet.edu.pk/index.php/muetrj/article/view/2618 |
| _version_ | 1797974202935934976 |
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| author | Murali Krishna Enduri Abdur Rashid Sangi Satish Anamalamudi Ramanadham Chandu Badrinath Manikanta Kallam Yogeshvar Reddy Panchumarthi Lovely Yeswanth Suda Kiran Sai Reddy Gogineni Asish Karthikeya |
| author_facet | Murali Krishna Enduri Abdur Rashid Sangi Satish Anamalamudi Ramanadham Chandu Badrinath Manikanta Kallam Yogeshvar Reddy Panchumarthi Lovely Yeswanth Suda Kiran Sai Reddy Gogineni Asish Karthikeya |
| author_sort | Murali Krishna Enduri |
| collection | DOAJ |
| description | With the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is because users post the reply comments in social media websites based on the experiences about an event or product. Furthermore, people are interested to know whether the majority of potential buyers will have a positive or negative experience on the event or the product. This kind of classification in general can be attained through Sentiment Analysis which inputs unstructured text comments about the product reviews, events, etc., from all the reviews or comments posted by users. This further classifies the data into different categories namely positive, negative or neutral opinions. Sentiment analysis can be performed by different machine learning models like CNN, Naive Bayes, Decision Tree, XgBoost, Logistic Regression etc. The proposed work is compared with the existing solutions in terms of different performance metrics and XgBoost outperforms out of all other methods. |
| first_indexed | 2024-04-11T04:16:15Z |
| format | Article |
| id | doaj.art-7e159bf44eff480b9d9127aeb7e38cb2 |
| institution | Directory Open Access Journal |
| issn | 0254-7821 2413-7219 |
| language | English |
| last_indexed | 2024-04-11T04:16:15Z |
| publishDate | 2023-01-01 |
| publisher | Mehran University of Engineering and Technology |
| record_format | Article |
| series | Mehran University Research Journal of Engineering and Technology |
| spelling | doaj.art-7e159bf44eff480b9d9127aeb7e38cb22022-12-31T13:04:47ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192023-01-0142120721510.22581/muet1982.2301.192618Comparative study on sentimental analysis using machine learning techniquesMurali Krishna Enduri0Abdur Rashid Sangi1Satish Anamalamudi2Ramanadham Chandu Badrinath Manikanta3Kallam Yogeshvar Reddy4Panchumarthi Lovely Yeswanth5Suda Kiran Sai Reddy6Gogineni Asish Karthikeya7Department of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur IndiaDepartment of Computer Science, College of Science and Technology, Wenzhou-Kean University, Ouhai, Wenzhou Zhejiang ChinaDepartment of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur IndiaDepartment of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur IndiaDepartment of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur IndiaDepartment of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur IndiaDepartment of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur IndiaDepartment of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur IndiaWith the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is because users post the reply comments in social media websites based on the experiences about an event or product. Furthermore, people are interested to know whether the majority of potential buyers will have a positive or negative experience on the event or the product. This kind of classification in general can be attained through Sentiment Analysis which inputs unstructured text comments about the product reviews, events, etc., from all the reviews or comments posted by users. This further classifies the data into different categories namely positive, negative or neutral opinions. Sentiment analysis can be performed by different machine learning models like CNN, Naive Bayes, Decision Tree, XgBoost, Logistic Regression etc. The proposed work is compared with the existing solutions in terms of different performance metrics and XgBoost outperforms out of all other methods.https://publications.muet.edu.pk/index.php/muetrj/article/view/2618 |
| spellingShingle | Murali Krishna Enduri Abdur Rashid Sangi Satish Anamalamudi Ramanadham Chandu Badrinath Manikanta Kallam Yogeshvar Reddy Panchumarthi Lovely Yeswanth Suda Kiran Sai Reddy Gogineni Asish Karthikeya Comparative study on sentimental analysis using machine learning techniques Mehran University Research Journal of Engineering and Technology |
| title | Comparative study on sentimental analysis using machine learning techniques |
| title_full | Comparative study on sentimental analysis using machine learning techniques |
| title_fullStr | Comparative study on sentimental analysis using machine learning techniques |
| title_full_unstemmed | Comparative study on sentimental analysis using machine learning techniques |
| title_short | Comparative study on sentimental analysis using machine learning techniques |
| title_sort | comparative study on sentimental analysis using machine learning techniques |
| url | https://publications.muet.edu.pk/index.php/muetrj/article/view/2618 |
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