Sentiment Classification of Roman Urdu Opinions Using Naive Bayesian, Decision Tree and KNN Classification Techniques
| المصدر: | مجلة جامعة الملك سعود - علوم الحاسب والمعلومات |
|---|---|
| الناشر: | جامعة الملك سعود |
| المؤلف الرئيسي: | Bilal, Muhammad (Author) |
| مؤلفين آخرين: | Israr, Huma (Co-Author), Khan, Amin (Co-Author), Shahid, Muhammad Shafiq (Co-Author) |
| المجلد/العدد: | مج28, ع3 |
| محكمة: | نعم |
| الدولة: |
السعودية |
| التاريخ الميلادي: |
2016
|
| الصفحات: | 330 - 344 |
| DOI: |
10.33948/0584-028-003-001 |
| ISSN: |
1319-1578 |
| رقم MD: | 973896 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | science |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Roman Urdu | Opinion Mining | Bag of Words | Naive Bayes | Decision Tree | K-Nearest Neighbor
|
| رابط المحتوى: |
| المستخلص: |
Sentiment mining is a field of text mining to determine the attitude of people about a particular product, topic, politician in newsgroup posts, review sites, comments on facebook posts twitter, etc. There are many issues involved in opinion mining. One important issue is that opinions could be in different languages (English, Urdu, Arabic, etc.). To tackle each language according to its orientation is a challenging task. Most of the research work in sentiment mining has been done in English language. Currently, limited research is being carried out on sentiment classification of other languages like Arabic, Italian, Urdu and Hindi. In this paper, three classification models are used for text classification using Waikato Environment for Knowledge Analysis (WEKA). Opinions written in Roman-Urdu and English are extracted from a blog. These extracted opinions are documented in text files to prepare a training dataset containing 150 positive and 150 negative opinions, as labeled examples. Testing data set is supplied to three different models and the results in each case are analyzed. The results show that Naϊve Bayesian outperformed Decision Tree and KNN in terms of more accuracy, precision, recall and F-measure. |
|---|---|
| ISSN: |
1319-1578 |
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