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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:
صورة الغلاف QR قانون
حفظ في:
LEADER 02524nam a22002657a 4500
001 1716759
024 |3 10.33948/0584-028-003-001 
041 |a eng 
044 |b السعودية 
100 |9 525160  |a Bilal, Muhammad  |e Author 
245 |a Sentiment Classification of Roman Urdu Opinions Using Naive Bayesian, Decision Tree and KNN Classification Techniques 
260 |b جامعة الملك سعود  |c 2016 
300 |a 330 - 344 
336 |a بحوث ومقالات  |b Article 
520 |b 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. 
653 |a اللسانيات الحاسوبية  |a اللغة الرومانية  |a اللغة الأردية  |a اللغة الإنجليزية  |a نظرية بايز 
692 |b Roman Urdu  |b Opinion Mining  |b Bag of Words  |b Naive Bayes  |b Decision Tree  |b K-Nearest Neighbor 
700 |9 525161  |a Israr, Huma  |e Co-Author 
700 |9 525163  |a Khan, Amin  |e Co-Author 
773 |c 001  |e Journal of King Saud University (Computer and Information Sciences)  |f Maǧalaẗ ǧamʼaẗ al-malīk Saud : ùlm al-ḥasib wa al-maʼlumat  |l 003  |m مج28, ع3  |o 0584  |s مجلة جامعة الملك سعود - علوم الحاسب والمعلومات  |v 028  |x 1319-1578 
700 |9 525162  |a Shahid, Muhammad Shafiq  |e Co-Author 
856 |u 0584-028-003-001.pdf 
930 |d y  |p y  |q n 
995 |a science 
999 |c 973896  |d 973896 

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