Name
Abstract | ||
|---|---|---|
With the widespread use of the internet, the size of the text data increases day by day. Poems can be given as an example of the growing text. In this study, we aim to classify poetry according to poet. Firstly, data set consisting of three different poetry of poets written in English have been constructed. Then, text categorization techniques are implemented on it. Chi-Square technique are used for feature selection. In addition, five different classification algorithms are tried. These algorithms are Sequential minimal optimization, Naive Bayes, C4.5 decision tree, Random Forest and k-nearest neighbors. Although each classifier showed very different results, over the 70% classification success rate was taken by sequential minimal optimization technique. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Information Retrieval | Data mining,Decision tree,Feature selection,Naive Bayes classifier,Computer science,Artificial intelligence,Statistical classification,Sequential minimal optimization,Classifier (linguistics),Random forest,Machine learning,The Internet |
DocType | Volume | Citations |
Journal | abs/1810.11414 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sahin, Durmus Ozkan | 1 | 0 | 1.35 |
| Oguz Emre Kural | 2 | 0 | 0.34 |
| Erdal Kılıç | 3 | 15 | 3.78 |
| Armagan Karabina | 4 | 0 | 0.34 |