Name
Title | ||
|---|---|---|
Subjectivity Classification of Filipino Text with Features Based on Term Frequency -- Inverse Document Frequency |
Abstract | ||
|---|---|---|
Subjectivity classification classifies a given document if it contains subjective information or not, or identifies which portions of the document are subjective. This research reports a machine learning approach on document-level and sentence-level subjectivity classification of Filipino texts using existing machine learning algorithms such as C4.5, Na茂ve Bayes, k-Nearest Neighbor, and Support Vector Machine. For the document-level classification, result shows that Support Vector Machines gave the best result with 95.06% accuracy. While for the sentence-level classification, Na茂ve Baves gave the best result with 58.75% accuracy. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IALP.2013.40 | IALP |
Keywords | Field | DocType |
support vector machines,sentence-level subjectivity classification,existing machine,inverse document frequency,filipino text,best result,subjectivity classification,subjective information,term frequency,sentence-level classification,support vector machine,document-level classification,learning artificial intelligence,natural language processing,text analysis | Structured support vector machine,One-class classification,Computer science,Artificial intelligence,Natural language processing,Multiclass classification,Naive Bayes classifier,Pattern recognition,tf–idf,Support vector machine,Relevance vector machine,Linear classifier,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ralph Vincent J. Regalado | 1 | 1 | 1.09 |
| Jenina L. Chua | 2 | 0 | 0.34 |
| Justin L. Co | 3 | 0 | 0.34 |
| Thomas James Z. Tiam-Lee | 4 | 0 | 0.34 |