LSI with Support Vector Machine for Text Categorization – a practical example with Python
| dc.contributor.author | Gomes Da Costa Cavalcanti, Joao Henrique | |
| dc.contributor.author | Menyhárt, József | |
| dc.date.accessioned | 2021-11-19T20:28:19Z | |
| dc.date.available | 2021-11-19T20:28:19Z | |
| dc.date.issued | 2021-11-19 | |
| dc.description.abstract | Artificial intelligence is becoming a powerful tool of modernity science, there is even a science consensus about how our society is turning to a data-driven society. Machine learning is a branch of Artificial intelligence that has the ability to learn from data and understand its behavers. Python programming language aiming the challenges of this new era is becoming one of the most popular languages for general programming and scientific computing. Keeping all this new era circumstances in mind, this article has as a goal to show one example of how to use one supervised machine learning method, Support Vector Machine, and to predict movie’s genre according to its description using the programming language of the moment, python. Firstly, Omdb official API was used to gather data about movies, then tuned Support Vector Machine model for Latent semantic indexing capable of predicting movies genres according to its plot was coded. The performance of the model occurred to be satisfactory considering the small dataset used and the occurrence of movies with hybrid genres. Testing the model with larger dataset and using multi-label classification models were purposed to improve the model. | en |
| dc.description.abstract | Artificial intelligence is becoming a powerful tool of modernity science, there is even a science consensus about how our society is turning to a data-driven society. Machine learning is a branch of Artificial intelligence that has the ability to learn from data and understand its behavers. Python programming language aiming the challenges of this new era is becoming one of the most popular languages for general programming and scientific computing. Keeping all this new era circumstances in mind, this article has as a goal to show one example of how to use one supervised machine learning method, Support Vector Machine, and to predict movie’s genre according to its description using the programming language of the moment, python. Firstly, Omdb official API was used to gather data about movies, then tuned Support Vector Machine model for Latent semantic indexing capable of predicting movies genres according to its plot was coded. The performance of the model occurred to be satisfactory considering the small dataset used and the occurrence of movies with hybrid genres. Testing the model with larger dataset and using multi-label classification models were purposed to improve the model. | hu |
| dc.format | application/pdf | |
| dc.identifier.citation | International Journal of Engineering and Management Sciences, Vol. 6 No. 3 (2021) , 18-29 | |
| dc.identifier.doi | https://doi.org/10.21791/IJEMS.2021.3.2. | |
| dc.identifier.eissn | 2498-700X | |
| dc.identifier.issue | 3 | |
| dc.identifier.jtitle | International Journal of Engineering and Management Sciences | |
| dc.identifier.uri | https://hdl.handle.net/2437/325186 | en |
| dc.identifier.volume | 6 | |
| dc.language | en | |
| dc.relation | https://ojs.lib.unideb.hu/IJEMS/article/view/10026 | |
| dc.rights.access | Open Access | |
| dc.rights.owner | Joao Henrique Gomes Da Costa Cavalcanti, József Menyhárt | |
| dc.subject | Machine learning | en |
| dc.subject | Support Vector Machine | en |
| dc.subject | Text Categorization | en |
| dc.subject | Machine learning | hu |
| dc.subject | Support Vector Machine | hu |
| dc.subject | Text Categorization | hu |
| dc.title | LSI with Support Vector Machine for Text Categorization – a practical example with Python | en |
| dc.type | folyóiratcikk | hu |
| dc.type | article | en |
| dc.type.detailed | idegen nyelvű folyóiratközlemény hazai lapban | hu |
Fájlok
Eredeti köteg (ORIGINAL bundle)
1 - 1 (Összesen 1)