A Simple Study of Webpage Text Classification Algorithms for Arabic ...

A Simple Study of Webpage Text Classification Algorithms for Arabic and English Languages Sumaia Mohammed AL-Ghuribi and Saleh Alshomrani Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia [email protected] , [email protected] Abstract— Webpage text Classification is an important problem that has been studied through different approaches and algorithms. It aims to assign a predefined category to a Webpage based on its content and linguistic features. It has many applications such as word sense disambiguation, document indexing, text filtering, Webpages hierarchical categorization and document organization. This study is a part of a work in progress, in which we are targeting to develop Bilanguages algorithm for classifying Arabic and English Webpage text and can perform accurate and efficient in both languages. It aims at providing a simple overview of many approaches that constructed for classifying Arabic and English Webpage documents. In this survey, the widely used algorithms for text classification are given with a comparison of the recent researches in classification field for Arabic and English languages to conclude which is the best algorithm that we can apply for both Arabic and English Languages. Keywords— Webpage text classification, linguistic features, Arabic language, Classification Algorithms .

I.

INTRODUCTION

Nowadays we have noticed an explosive growth on the Internet, with billions of Webpages on every topic and can be easily accessible through the Web. The rapid growth of Webpages, led to use data mining and its applications to organize Web documents and make them more useful for the users. One of the data mining applications is text classification (TC), Text classification is an old research area, it gained more interest in the 1990's because of the increasing amount of textual information and the improvement of computer technologies which enabled a more sophisticated algorithms to be implemented [19].Text classification (TC) is a Natural Language processing (NLP) problem; it can be defined as the assignment of unclassified document to one or more predefined categories based on their content [19]. It is also referred as Text categorization, document categorization, document classification or topic spotting [3]. TC techniques are used in many applications such as document filtering, spam filtering, e-mail filtering, news monitoring, mail routing, indexing, document organization and retrieval, and opinion mining. Generally, TC process goes through three phases: Data pre-processing, text classification and evaluation. Data preprocessing phase contains two tasks, document representation and feature selection. The main ways for representing document are a bag of words, in which a document is represented as a set of words, together with their associated frequency in the document or to represent text document directly as strings, in which each document is a sequence of words [30]. Most text classification methods use the bag-of-words representation because of its simplicity for classification purposes [30]. Feature selection achieves two main

goals [13]. First, it makes the training applied to a classifier more efficient by decreasing the high dimensionality of effective vocabulary. Second, feature selection often increases classification accuracy by redaction rare terms. In the next step of classification [19] , the documents are split into training and testing documents, the training documents are used to train the system (i.e. learn the system) to recognize different patterns of categories, the testing documents are used to evaluate the system, the process of categorization depends on the algorithm used using evaluation measures i.e. recall, precision, F1-measure. There are a lot of algorithms that are used as classifiers but the main algorithms (Classifiers) that are used widely are [42, 27, 30] Decision Tree Classifier, Support Vector Machine Classifier, Naïve Bayes Classifier, Neural Networks Classifier and K-Nearest Neighbors Classifier. Most of the TC researches are designed and tested for English languages; some TC approaches were carried out for other European languages such as German, Italian, Spanish, Chinese and Japanese. However, there is a little TC work that carried out for Arabic language [3] due to the complex and rich nature of the Arabic language which faces many challenges when someone is interest in creating Arabic classifier. Some of the Arabic language characteristics are as follow, Arabic is a very rich language with complex morphology. It belongs to the family of Semitic languages. It differs from Latin languages morphologically, syntactically and semantically. The writing system of Arabic has 25 consonants and three long vowels that are written from right to left and change shapes according to their position in the word. Additionally, Arabic has short vowels (diacritics) written above and under a consonant to give it its desired sound and hence give a word a desired meaning, like fatha, kasra and damma. The Arabic language consists of three types of words; nouns, verbs and particles. Nouns and verbs are derived from a limited set of about 10,000 roots [43]. Furthermore, a stem may accept prefixes and/or suffixes in order to form the word [44]. So, Arabic language is highly derivative where tens or even hundreds of words could be formed using only one root. Furthermore, a single word may be derived from multiple roots [45]. In this study the main algorithms for classifying Arabic and English Webpage document are discussed and a comparison about the recent researches in classification using the main algorithms mentioned above or any other algorithms are presented to find which algorithm is best for Bi-languages classifier. The rest of this paper is organized as follow: section II presents five main algorithms for classifying Webpage document; section III gives a comparison among the recent researches in classification fields and finally we conclude this study in Section IV.

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II.

WEBPAGE TEXT CLASSIFICATION ALGORITHMS

A. Decision Tree Classifier Decision trees are one of the most powerful used inductive learning methods. They are one of the data mining induction approaches which are considered as one of the most widely used in classification process. Their robustness to noisy data and their capability to learn disjunctive expressions seem suitable for document classification [3]. They are designed with the use of a hierarchical division of the underlying data space with the use of different text features [30]. They are performed in two phases either tree building (top-down manner) or tree pruning (bottom-up manner). Decision tree method takes the data described by its features as input. It partitions the data of records recursively using breadth-first approach or depthfirst greedy approach until all the data items have assigned to a particular class. The output is a decision which assigns the suitable class for the input data. Some of the most well-known decision tree algorithms are [24] IDE3 (Iterative Dichotomiser 3) decision tree algorithm was introduced in 1986 by Quinlan Ross, C4.5 algorithm is an improvement of IDE3 algorithm, developed by Quinlan Ross (1993), CART (Classification and regression trees) was introduced by Breiman, (1984). SLIQ (Supervised Learning In Ques) was introduced by Mehta et al, (1996) and SPRINT (Scalable Parallelizable Induction of decision Tree algorithm) was introduced by Shafer et al, 1996.

B. Support Vector Machine Classifier Support Vector Machines (SVMs) is a new class of machine learning techniques which first introduced by Vapnik [32] and has been introduced in text classification by Joachims [6]. SVM is one of the most robust and successful classification Algorithms [40].The first use of SVM for Arabic text classification was presented by Abdelwadood Moh'd A MESLEH [41]. SVM is based on the principle of structural risk minimization [4]. SVM Classifiers attempt to partition the data space with the use of linear or non-linear delineations between the different classes [30]. The key in such classifiers is to determine the optimal boundaries between the different classes and use them for the purposes of classification [30]. Support Vector Machines have been applied successfully in many text classification tasks because of their principle advantages as follow[33]: robust in high dimensional spaces, in which over fitting does not affect so much the computation of the final decision margin; any feature is important even some features that could be considered as irrelevant ones have been found to be good when calculating the margin ; robust when there is a sparsely of samples and most text categorization problems are linearly separable. Additionally, SVM method is flexible and can easily be combined with interactive userfeedback methods [37].

C. Naïve Bayes Classifier A naive Bayes (NB) classifier is a simple probabilistic classifier which is used in classification process. It is based on applying Bayes' theorem which is a formula that calculates a probability by counting the frequency of values and combinations of values in the historical data, with strong (naive) independence assumptions. The naive part of the classifier comes from the simplifying assumption that all terms are

conditionally independent of each other given a category. Because of this independence assumption, the parameters for each term can be learned separately and this simplifies and speeds the computation operations compared to nonnative Bayes classifiers [6]. NB classifier attempts to build a probabilistic classifier based on modeling the underlying word features in different classes. The idea is then to classify text based on the posterior probability of the documents belonging to the different classes on the basis of the word presence in the documents [30]. There are two common models for NB text classification, discussed by Mccallum and Nigam [38], multinomial model and multivariate Bernoulli model [6]. Both models essentially compute the posterior probability of a class, based on the distribution of the words in the document [30]. Some advantages of Naive Bayes are, NB requires a small amount of training data to estimate the parameters necessary for classification. Also, NB algorithm affords fast, highly scalable model building and scoring, and can be used for both binary and multiclass classification problems.

D. Neural Networks Classifier Neural networks have considered as an important tool for classification. The recent vast research activities in neural classification have established that neural networks are a promising alternative to various conventional classification methods [39]. Neural network algorithm consists of an input layer, one or more hidden layers and an output layer [40].The basic unit in a neural network is a neuron or unit. The inputs to the network correspond to the attributes measured for each training tuple which are fed into the units making up the input layer. They are then weighted and fed simultaneously to a hidden layer. The number of hidden layers is arbitrary, although usually only one. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network's prediction [40]. Neural networks have many advantages , we can summarize some of them as [39] states as follow; first, they are data driven self-adaptive methods in that they can adjust themselves to the data without any explicit specification of functional or distributional form for the underlying model. Second, they are universal functional approximates in that neural networks can approximate any function with arbitrary accuracy. Third, neural networks are nonlinear models, which makes them flexible in modeling real world complex relationships. Finally, neural networks are able to estimate the posterior probabilities, which provide the basis for establishing classification rule and performing statistical analysis.

E. K-Nearest Neighbors Classifier K-nearest neighbor (k-NN) classification is a nonparametric lazy learning algorithm and a well-known statistical approach which considered among the oldest nonparametric classification algorithms [40]. It is based on closest training examples in the feature space. The main thesis in K-NN is that documents which belong to the same class are likely to be close to one another based on similarity measures such as the dot product or the cosine metric [30]. KNN assumes that the data is in a feature space, since the points are in feature space, they have a notion of distance. Also it assumes that each of the training data consists of a set of vectors and class label associated with each vector. A single number "k" is given; this number

decides how many neighbors influence the classification. Although, K-NN classifier is a very simple classifier that works well on basic recognition problems, it has a set of drawbacks [6]; K-NN is a lazy learning example-based method that does not have an off-line training phase. The main computation is the on-line scoring of training documents given a test document in order to find the k nearest neighbors, this makes k-NN not efficient because nearly all computation takes place at classification time rather than when the Approach 1. (Kwon & Lee,2000) 2. ( Nigam et al.,2000) 3. (Tsukada et al.,2001) 4. (Sun et al.,2002) 5. (Selamat et al.,2003) 6. (El Kourdi et al.,2004) 7. (Al-Shalabi et al.,2006) 8. (Chandra&Paul,2006) 9. (El-Halees,2007) 10. (Shi et al.,2008) 11. (Zhongli&Zhijing,2008) 12. (Thabtah et al.,2008) 13. (Al-Shalab&Obeidat,2008)

14. (Al-Harbi et al.,2008)

16. (Thabtah et al.,2009) 17. (Yazdani et al.,2009) 18. (Kamruzzaman,2010) 19. (Zhong & Zou , 2011) 20. (Alsaleem,2011)

22. (Youquan et al.,2011)

23. (Chantar et al.,2011) 24. (Zhaohui et al.,2011)

COMPARISON OF WEBPAGE TEXT CLASSIFICATION APPROACHES

Evaluation Data Set

Document Represent.

Language

KNN

Bag-of- words

English

Expectation-Maximization (EM) & NB

Set-of-words

English

Decision tree

Bag-of- words

English

SVM

Set-of-words

English

Neural network

Bag-of- words

English

NB

Bag-of- words

Arabic

KNN

Bag-of- words

Arabic

Decision trees

Set-of-words

English

Maximum Entropy

Set-of-words

Arabic

(hybrid) KNN &SVM

Bag-of- words

NB KNN KNN SVM

Precision

Recall

F1measure

8,702

N/A

N/A

N/A

10

82

N/A

N/A

14

79.9

48.2

60.14

4

N/A

N/A

56.2

11

84.10

N/A

N/A

5

68.78

N/A

N/A

6

95

95

95

N/A

75.7

N/A

N/A

N/A

6

80.34

80.48

80.41

English

N/A

N/A

90

97

93

Bag-of- words

English

4,338 Webpages

11

91.38

94.09

92.13

Bag-of- words

Arabic

N/A

6

N/A

N/A

94.91

Arabic

1445 Webpages

9

Arabic

17,658 Webpages

7

Bag-of- words N-Grams Bag-of- words Bag-of- words

Arabic

NB

Set-of-words

Arabic

NB & an extended version of Hidden Markov Model

Bag-of- words

English

Artificial Neural Network

Relational

English

SVM

Bag-of- words

English

Set-of-words

Arabic

SVM

Measure Factors # of Classes

SVM

NB 21. (Ting et al.,2011)

III.

Classifier

Decision tree 15. (Gharib et al.,2009)

training examples are first encountered. Moreover, k-NN classifier has a major drawback of selecting the value of k; the success of classification is very much dependent on this value.

Data Set 32,442 Webpages UseNet, WebKB, newswire 5 domains of Yahoo 4159 Webpages 784 Webpages 300 Webpages 621 Webpages 12 datasets form UCI

70.66

64.54

66.88

87.62

63.41

73.57

68.65

N/A

N/A

78.42

N/A

N/A

6

N/A

N/A

~ 99

1132 Webpages 1562 Webpages 86842 Webpages 500 homepages 3160 Webpages

6

~ 73

~ 72

~ 72

9

85.9

N/A

N/A

8

N/A

N/A

N/A

6

92.0

94.65

93.31

5121 Webpages

7

4000 Webpages 700 Webpages

77.9

77.8

77.8

74.1

74

74

4

95.6

95.5

95.5

5

92

N/A

N/A

NB

Set-of-words

English

NB

Bag-of- words

English

SVM NB Decision tree

Bag-of- words

Arabic

4381 Webpages

13

92.6 85.4 76.1

N/A N/A N/A

N/A N/A N/A

KNN

Bag-of- words words=links

English

3430 Webpages

9

99.6

71.3

83.0

25. (El-Halees,2011) 26. (Patil & Pawar ,2012) 27. (Mangai et al.,2012) 28. (Baraa et al. , 2012) 29. (ALZaghoul & Dhaheri,2013) 30. (Mangai et al.,2013)

KNN

Word/phrase

Arabic

NB

Set-of-words

English

Using Feature Intervals

Bag-of- words

English

Frequency Ratio Accumulation Method (FRAM)

N-Grams

Artificial Neural Network KNN

1143 Webpages 4887 Webpages

3

80.29

N/A

N/A

10

89.09

89.04

89.05

WebKB

2

93

N/A

N/A

Arabic

3172 Webpages

4

N/A

N/A

95.1

Bag-of- words

Arabic

2800 Webpages

7

N/A

N/A

~ 97

Bag-of- words

English

WebKB

N/A

96.31

N/A

N/A

Table 1: Comparison of Webpage text classification approaches. To determine which algorithm will be suitable in classifying both Arabic and English Webpage text and give high accuracy, we take the precision column above and make a graph as Figure1 shows.

100 95

Accuracy

90 85 80 75 70

2 3 5 6 7 8 9 10 11 B_13 N_13 S_14 D_14 16 17 19 S_20 N_20 21 22 S_23 N_23 D_23 24 25 26 27 30

65

Approach # FIGURE 1 ACCURACY OF SOME APPROACHES FOR CLASSIFYING WEBPAGE TEXT

From approaches [14, 20, 23] we can get that SVM classifier outperforms NB and Decision tree Classifiers. Additionally, high accuracy is appear clearly when SVM or KNN classifiers are used such as [24, 19, 15, 7] which the accuracy ranges between 92% and 99%. But it is difficult to compare these two classifiers unless they are tested to the same corpora. In [46] Ismail made a comparative study of two machine learning methods (KNN and SVM) on Arabic text classification , results show that SVM gives better f1-measure value and prediction time than KNN. Also in [47] MESLEH made a text classification system for Arabic language articles using SVM classifier and compare it with NB classifier and KNN classifier , results show that SVM outperforms the other classifiers. In [48] El-Halees applied machine learning classifiers (NB, SVM , KNN) to Arabic and English corpora , results prove that SVM gives best F1-measure and accuracy. Additionally, in [49] Rashid designed a tool for comparing different classification algorithms that are used to classify different Arabic texts and his experiments result that SVM gives higher accuracy than KNN. Also the same as in English text SVM outperforms KNN as Siolas's

experiments in [50].As a result, we can conclude that SVM classifier is the best choice for our Bi-languages Algorithm.

I.

CONCLUSION AND FUTURE WORK

With the enormous increasing of Webpages, Webpage text classification becomes one of the important topics to improve many applications while using Webpage as a source of knowledge. In this study, we presented the widely used algorithms for classifying Webpage text and make a comparison for the available researches in classifying Arabic and English Webpage documents to determine which classifier will enhance the Bi-languages algorithm that we want to create for classifying Arabic and English Webpage text. As a future work, we will try to construct a classifier which performs efficiently in Arabic and English Webpage text and gives accurate results in the two languages.

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