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An_Improved_Approach_for_Sentiment_Analysis_of_Arabic_Tweets_in_Twitter_Social_Media.pdf
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An Improved Approach for Sentiment Analysis of Arabic Tweets in Twitter Social Media
Hussain AlSalman Department of Computer Science
King Saud University Riyadh, Saudi Arabia
Abstract— Recently, sentiment analysis of social media contents is very important for opinion mining in several applications and different fields. Arabic sentiment analysis is one of the more complicated sentiment analysis tools of social media due to the informal noisy contents and the rich morphology of Arabic language. There is a number of works has been proposed for Arabic sentiment analysis. However, these works need an improvement in terms of effectiveness and accuracy. Consequently, in this paper, a corpus-based approach is proposed for Arabic sentiment analysis of tweets annotated as either negative or positive in twitter social media. The approach is based on a Discriminative multinomial naïve Bayes (DMNB) method with N-grams tokenizer, stemming, and term frequency–inverse document frequency (TF-IDF) techniques. The experiments are conducted using a set of performance evaluation metrics on a public twitter dataset to test the proposed sentiment analysis approach. Experimental results demonstrated the usefulness of the proposed approach. Furthermore, the comparison results showed that the approach outperformed the related work and improved the accuracy with 0.3%.
Keywords-Arabic sentiment analysis; Twitter social media; Machine learning; Discriminative multinomial naïve Bayes; N- grams tokenizer;
I. INTRODUCTION In recent years, sentiment analysis of media contents has
become increasingly a hot topic for opinion mining in several applications of social networks [1]. Sentiment analysis-based opinion mining can be performed by determining the feelings and behavior of a subject such as writer, speaker, or another regarding to an event or a certain topic. It can be used in several real-life applications [2]. For example, in business, the companies can use sentiment analysis methods for automatically gathering the customers’ opinions about the services or products. Additionally, in politics, the public reaction and orientation to political events can be it can inferred for decision making by the help of sentiment analysis tools. For many years, machine learning methods used in sentiment analysis [3-6] and also in many applications [7-14]. Arabic sentiment analysis is one of the more complicated sentiment analysis tools of social media due to the informal noisy contents and the rich morphology of Arabic language. Increasing the rate of reviews and comments by Arabic users in different social media channels makes the Arabic sentiment analysis methods are
gaining more attention and importance. Modern standard Arabic (MSA) is an Arabic script depends mostly on the Classic Arabic that is based on Arabic dialects like Mesopotamian, Khaliji, Egyptian, Syro-Palestinian, and Maghrebi [15]. Therefore, a number of challenges for Arabic sentiment analysis methods. In this context, a literature survey is introduced in [16]. This survey discussed many challenges related to Arabic sentiment analysis, such as Arabic regional dialect and diglossia phenomena. Other challenges come from classical Arabic and related to the nature of the morphology, combinations of syntax, and lexical entities [17-19]. In social media, one of the major challenge for Arabic sentiment analysis research is the use of dialects and the absence of Arabic language standardization [20-22]. Beside the dialectical diversity, the richness of Arabic language is another issue that restrains the task of text analysis [23]. Arabic sentiment analysis can analyze the contents on several levels: sentence level, document level, and topic level. For instance in twitter, the sentence level-based Arabic sentiment analysis can determine the polarity of tweet that can be negative, positive, or neutral.
Duwairi and Qarqaz [24] proposed to apply a three of machine learning classifiers are support vector machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) on a twitter dataset of 2591 tweets. The experimental results showed that SVM achieves a highest precision result and KNN attains a highest recall result. Refaee and Rieser [22] collected a twitter corpus dataset of 8,868 Arabic tweets labelled and annotated manually for sentiment and subjectivity analysis. In [25], the authors introduced a lexicon-based and corpus-based Arabic sentiment analysis approach on string vectors of a dataset consisted of 2000 tweets. Shoukry and Rafea [26] presented a sentence-level Arabic sentiment analysis approach using a dataset contains a 1000 tweets collected from twitter social media. Aly and Atiya [27] offered a large-scale Arabic book reviews dataset (LABR) contained 63257 instances. Abdul- Mageed and Diab [28] presented a multi-genre corpus from Wikipedia for sentiment analysis and modern standard Arabic subjectivity evaluation. Rushdi-Saleh et al. [29] introduced a corpus-based sentiment analysis study on an opinion corpus for Arabic (OCA) dataset of 500 feeds from movies source. Zaidan and Callison-Burch [30] investigated the informal Arabic that has a high dialectal content by collecting an annotated Arabic online commentary (AOC) dataset of 1.4 million samples collected from Arabic online newspapers. Alomari et al. [31]
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introduced a study for sentiment analysis of Arabic tweets using NB and SVM machine learning classifiers with TF-IDF method. The authors reported that the performance of SVM classifier outperforms the performance of NB classifier. Heikal et al. [32] proposed an ensemble model that combines the convolutional neural network (CNN) model with long short-term memory (LSTM) model. The model was evaluated on the Arabic sentiment tweets dataset (ASTD) to classify the Arabic tweets as positives or negatives with F-score of 64.46%.
In this paper, an improved corpus-based Arabic sentiment analysis approach is proposed using a discriminative multinomial naïve Bayes (DMNB) classifier with 4-grams tokenizer, stemming, and term frequency–inverse document frequency (TF-IDF) techniques. A 5-fold cross-validation method is used for evaluating the experimental results. The paper also compares the DMNB classifier used in proposed approach with some other machine learning classifiers used in related work on the same dataset.
The rest of the paper is organized as follows. In Section 2, the proposed approach of Arabic sentiment analysis will be explained in more detail. Section 3 introduces the experimental results on a public twitter corpus dataset [25] for evaluating the proposed approach. Finally, Section 4 presents the conclusions of this study.
II. PROPOSED ARABIC SENTIMENT ANALYSIS APPROACH The proposed Arabic sentiment analysis approach consists
of two main steps: A) tweet processing and normalization and B) tweet classification, as seen in Figure 1. The input of the approach is Arabic tweets and the output is the polarity of tweets that can be negatives or positives.
Figure 1. The main steps of the proposed approach with its input and output.
The steps of the proposed approach are explained in the following subsections.
A. Tweet Processing and Normalization In tweet processing and normalization step, the words of
tweet are tokenized using 4-grams technique. Then, the tokenized words are stemmed using Khoja-stemmer to remove stop words. Finally, the term frequency with inverse document frequency transform (TF-IDFT) is used to compute the Arabic words occurrences.
B. Tweet Classification In this step, the discriminative multinomial naïve Bayes
(DMNB) classifier is applied to classify the Arabic tweets to positives and negatives. The DMNB was proposed and explained in [33]. It integrates the generative and discriminative learning to reflect the discrimination of classification and maintain the words frequencies. The tweet classification can be performed by two stages: training and testing stage. For the proposed approach, these two stages are implemented using a 5- fold cross-validation technique.
III. EXPERIMENTAL RESULTS Before The results are obtained by conducting the
experiments on a public twitter corpus dataset [25]. The dataset consists of 2000 Arabic tweets labelled by two different classes (negative and positive). The distribution of tweets according their class label in the corpus dataset is displayed in Figure 2.
Figure 2. The number and distribution of tweets in the twitter corpus dataset.
The experiments are implemented using a Waikato Environment for Knowledge Analysis (WEKA) machine learning tool [34]. To obtain the results of proposed approach for the 5-fold cross-validation technique, four evaluation metrics (Accuracy, Recall, Precision, and F-score) are used. These four metrics are defined as: = ( + )( + + + ) (1) = ( + ) (2) = ( + ) (3)
1000 50%
1000 50%
Positive Negative
Arabic contents (Tweets or Comments)
Tweet Preprocessing and Normalization
Tweet Classification
Polarity of tweets (Positive or Negative)
Classification
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F-Score = 2 × ( × )( + ) (4)
The experimental results are recorded based on the average of testing results for the five test sets generated during the 5-fold cross-validation technique. The number of iterations for the DMNB classifier is initialized to have the value 7. Table I displays the confusion matrix of Arabic tweets classification for samples included in 5-fold testing sets. Table 2 demonstrates the results of other evaluation metrics for negative and positive tweets.
TABLE I. CONFUSION MATRIX OF ARABIC TWEETS CLASSIFICATION FOR THE DMNB CLASSIFIER.
Predicted Actual
Negative Positive
Negative 898 102 Positive 148 852
TABLE II. RESULTS OF EVALUATION METRICS FOR ARABIC TWEETS CLASSIFICATION USING THE DMNB CLASSIFIER.
Class Name
Recall Precision F-
Score Accuracy
Negative 0.898 0.859 0.878
87.5% Positive 0.852 0.893 0.872 Weighted Avg.
0.875 0.876 0.875
From Tables I and II, we can notice that the DMNB classifier achieves 0.898 of recall metric for the negative tweets and 0.893 of precision metric for the positive tweets. In addition, the approach attains 0.875 of weighted average recall and 0.876 of weighted average precision for both negative and positive tweets. The f-score and accuracy results have the same value 87.5% because the data samples of both classes are balanced.
To compare the experimental results of the proposed approach, Table III demonstrates the accuracy of the DMNB classifier used in the proposed approach with the accuracy results of the classifiers used in the related work [25] on the same dataset.
TABLE III. COMPARISON RESULTS OF ACCURACY BETWEEN THE DMNB CLASSIFIER USED IN THE PROPOSED APPROACH AND OTHER CLASSIFIERS USED
IN RELATED WORK.
Study Stemming
Method Classifier Accuracy
Abdulla et al. [25] Light-stemmer
SVM 87.2%
NB 81.3%
KNN 51.45%
D-tree 50% This study Khoja-stemmer DMNB 87.2%
From Table III, we can notice that the DMNB classifier of proposed approach attains a high accuracy rate compared to the other classifiers in [25].
IV. CONCLUSIONS AND FUTURE WORK In this paper, an improved approach is proposed for Arabic
sentiment analysis by classifying the polarity of tweets in twitter social media. The approach is based on a discriminative multinomial naïve Bayes (DMNB) method with 4-grams tokenizer, stemming, and term frequency–inverse document frequency (TF-IDF) techniques. It also consists of two main steps: A) tweet processing and normalization and B) tweet classification. The experiments are implemented in WEKA machine learning tool by using a public twitter corpus dataset and 5-fold cross-validation technique. The dataset consists of 2000 Arabic tweets labelled by two different classes (negative and positive). The experimental results showed that the proposed approach improved the results of related work. In future work, feature selection and reduction algorithms will be used for further improvements.
ACKNOWLEDGMENT
This work was funded through the Research Center of the College of Computer and Information Sciences (CCIS) at King Saud University.”
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/HRV (Za stvaranje Adobe PDF dokumenata pogodnih za pouzdani prikaz i ispis poslovnih dokumenata koristite ove postavke. Stvoreni PDF dokumenti mogu se otvoriti Acrobat i Adobe Reader 5.0 i kasnijim verzijama.) /HUN <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> /ITA (Utilizzare queste impostazioni per creare documenti Adobe PDF adatti per visualizzare e stampare documenti aziendali in modo affidabile. I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 5.0 e versioni successive.) /JPN <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> /KOR <FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020be44c988b2c8c2a40020bb38c11cb97c0020c548c815c801c73cb85c0020bcf4ace00020c778c1c4d558b2940020b3700020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e> /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken waarmee zakelijke documenten betrouwbaar kunnen worden weergegeven en afgedrukt. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR <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> /POL 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<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> /ENU (Use these settings to create Adobe PDF documents suitable for reliable viewing and printing of business documents. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) >> >> setdistillerparams << /HWResolution [600 600] /PageSize [612.000 792.000] >> setpagedevice
