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Model-based_sentiment_analysis_of_customer_satisfaction_for_the_Jordanian_telecommunication_companies.pdf
Model-based Sentiment Analysis of Customer Satisfaction for the Jordanian Telecommunication
Companies
Hassan Najadat Computer Information Systems
Department Jordan University of Science and
Technology Irbid, Jordan
Amnah Al-Abdi Computer Science Department
Jordan University of Science and Technology Irbid, Jordan
Yasmeen Sayaheen Computer Science Department
Jordan University of Science and Technology Irbid, Jordan
Abstract—Nowadays, social networks are considered to be the most useful data source for analysis trend. The data can be used for different goals, for instance, the commercial companies use the Facebook pages to discover their customer satisfaction of the provided services. This research targets sentiment analysis the customers' posts on the formal Facebook pages of the Jordanian telecommunication companies. The dataset of this paper is collected from three main Jordanian telecommunication companies: Orange, Zain, and Umniah. A supervised based approach including K Nearest Neighbors, Support Vector Machine, Naïve Bayes, and Decision Trees are applied and a comparison between them is provided in term of accuracy metrics. In this study, we introduce a model that is able to make a sentiment analysis of the customer posts on Facebook written in Jordanian dialect.
Keywords— Sentiment Analysis, telecommunication company, classification, Facebook posts, Jordanian dialect, customer satisfaction.
I. INTRODUCTION Understanding information and converting it into a
knowledge is a challenging issue in the history of big data. Recently, many studies are raising awareness in natural language processing for Arabic and its dialects. This growth of awareness can be recognized because of the extensive use of Arabic dialects [1]. In concepts of Arabic language dialect processing and specifically in sentiment understanding, this typically involves realizing different human languages which are obliquely related to appropriate human aspects and concepts. A very brief example of actual dissimilarities can be shown by a word مبسوط, mabsut, which has a positive insight in Egypt, however, it does not has the same insight in other Arabic dialects such as Iraq [2]. In this circumstance, the social websites such as Facebook and Twitter have been considered to be the source of useful data for trend analysis, taking into account the volume of the data and its popularity in different dialects. Currently, mining Facebook proved to be an appropriate area for a research, as it is a source of newly daily information [3]. Such enormous amounts of producing data may become useful to businesses, government, and others related creation essential decisions.
Competition between companies in the market is an old problem that becomes more challenging by the appearance of social media [4].
Customer satisfaction is a vital issue among companies in order to increase its revenue. The interviews and questionnaires are not applicable to measure the customer satisfaction, alternatively, extracting a customer satisfaction using sentiment analysis from their written reviews can be more practical these days.
Extraction customer feedback of telecom companies in Jordan will help the companies to enhance their services, focus on drawback and look toward improving customer satisfaction. The sentiment Analysis process tries automatically or semi-automatically to decide polarity of words embedded in user comments.
Commonly two approaches are used for sentiment analysis for all languages. Firstly, unsupervised based learning approaches, such approaches depend on sentiment lexicons. Secondly, supervised based learning approaches that depend on training data to build a model and use it to predict new unseen data [5, 6]. In our case analysis, every word in the customer comment is an important for running the sentiment analysis.
The research exploits supervised machine learning approaches, specific classification to sense the polarity of customer post written on the telecommunication company Facebook pages [7].
The rest of this paper is organized as follows: section 2 briefly introduces related works, section 3 describes the methodology and the experiments, Section 4 provides the results of the experiment and the discussion, Finally, section 5 concludes with future works.
II. RELATED WORK Abbasi et al [8] suggested entropy weighted based
machine learning to classify movie based on Arabic fans reviews. Classification is using features yielded the accuracy result is 93.62% to computation drawback cost. Farra et al [9] performed analysis for Arabic documents based on
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features of the sentence like frequency of words related to each class positive and negative. After making frequency vector, they applied a decision tree approach and they got a 62% accuracy.
Rushdi-Saleh et al [10] produced a new corpus that contains sets of movies review in Arabic and used n-grams based model that aids in classifying task using two classifiers Support Vector Machine (SVM) and Naïve Bayes (NB). The result showed that SVM achieved a higher accuracy with 90.73%.
El-Halees et al [11] collected set of posts related to three fields: learning, government, and sports. El-Halees used three classifiers to enhance the performance and attained the accuracy with 80%.
SAMAR et al [12] applied two-stage to make a classification, the first stage differentiates between subjective and objective sentence, the next stage classifies sentence either a positive or a negative label. The classifier was SVM for every stage. Shoukry et al [13] used Twitter to address sentiment analysis problem and cut out tweets to be 140 characters. The dialect was Egyptian with two classifiers: SVM and NB.
Mountassir et al [14] utilized n-grams based method with three classifiers: NB, SVM and K- Nearest Neighbor (KNN). The preprocessing stage included stemming, term frequency, and weighting based to enhance the classifications result for KNN and NB. The KNN performed the highest accuracy with 93%.
Haifa K. Aldayel et al [15] used the tweet and took 140 characters from every tweet for the Saudi Arabian dialect, they used a hybrid approach of lexicon-based approach and machine learning approach. SVM was used with 84% accuracy.
Sana Alowaidi et al [16] analyzed Arabic dialect using a supervised approaches including Naïve Bayes and SVM. They added a concept feature and the result showed that SVM gained accuracy with 95.63%.
III. METHODOLOGY AND EXPERIMENTS
A. Dataset The dataset of this paper is collected from the formal
Facebook pages of the three Jordanian telecommunication companies: Orange [17], Zain [18], and Umniah [19]. The dataset is collected using Netvizz which is a data collection application that enables the researchers to extract the data from Facebook with different modules such as group data, page data, page like network, page timeline images, search and link stats [20]. In our case, we are interested in the page data module.
The following steps were performed to extract the user’s text post from the formal Facebook pages of the telecommunication companies:
• Find the page id for each telecommunication company’s page through search module.
• Select the period of the extracted post which is in our case between 1st Apr. 2016 and 1st Apr. 2017.
• Get the full data.
• Download the zip archive in order to get the generated files.
• Find and extract the TAB file which contains “fullstas" in its name.
The TAB file contains 29 attributes: "type", "by", "post_id","post_link","post_message","picture","full_picture ","link","link_domain","post_published","post_published_sql ","comments_count_fb","shares_count_fb","comments_retrie ved","comments_replies","rea_NONE","rea_LIKE","rea_LO VE","rea_WOW","rea_HAHA","rea_SAD", "rea_ANGRY", and "rea_THANKFUL".
In this work, we collected 14332 instances as follows: 2199 instances from Orange, 7634 instances from Umniah and 4499 instances from Zain. Regarding the goal of our work in this paper, we are interested in "type", "by", and "post_message" attributes. The "type" attribute refers to the type of the post if it is either status or photo and we are only interested in the status type. The "by" attribute detects if the post is either by page owners or by users (the telecommunication customers) and we are interested only in the user’s posts. The "message_post" contains the text message if the post is in status type. At the end of the previous process, our dataset contains only one attribute which represents the post text of the status that was posted by the users.
B. Labeling The labeling process is the most difficult process in
which a new attribute is added to the dataset, we label it as "category", this attribute will detect to which category each instance in the dataset is belong, these categories include "opinion", "question", and "other".
The "opinion" category contains the posts which represent the user’s opinion to the provided services from the telecommunication company. The question category contains the different questions which the users posted them on the telecommunication Facebook page in order to get an answer. The "other" category contains any post that is neither a question nor an opinion.
In our work here, we interested only in the opinion posts, however, to obtain these posts, we manually label each instance as opinion, other, or question. The opinion category values are either "positive" or "negative". Positive means that a customer is satisfied with the provided services, and "negative" represents the opposite. It is important to mention that every word in the post is important for deciding to which category each post belongs. Since we are Jordanians, we have enough experience in Jordanian delicts and its words meaning, especially the words which indicates if the post is negative or positive. Also, the nature of our dataset is the reason behind the opinion category is either negative or positive (neutral is not included), because usually, the customer either impressed with the provided services or he
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dissatisfied. Within the manual labeling operation, many actions were performed such as removing English or any non-Arabic language posts since we are interested in Arabic posts only, remove the duplicated posts, and removing the empty posts.
As a result of the previous step, each instance in each company is labeled as positive, negative, other, or question. As we previously mentioned, we are interested only in the opinion posts (negative, positive), so the final dataset contains only the posts which were labeled as positive or negative. The final size for each company’s dataset is Orange with 758 instances (negative: 638, positive: 120), Umniah with 2338 instances (negative: 2198, positive: 140), Zain with 1131 instances (negative: 1026, positive: 105).
From the size of the final datasets it is clear that the majority of the user’s posts are not opinion posts, in fact, most of them were questions posts, also it is clear that the dataset for each company are not balanced, since the number of positive instances is not equal to the negative instances [21].
C. Model construction To construct our model, we use RapidMiner tool which is
a powerful machine learning tool that comes up with all datamining tasks from preprocessing, discovering knowledge and role, and excellent visualization of results that come from classification, clustering, and association. RapidMiner is used extensively in industrial, education and business fields through impeding algorithms, models and data schemas in two script tool R and WEKA [22]. We applied different classification algorithms including naïve Bayes, SVM, Decision tree, and KNN. Firstly, we applied the previous classification algorithms on each company dataset separately without sampling, then we applied the classifiers to each company’s dataset separately with sampling. The last scenario is applied to the whole dataset by combining the three datasets in one dataset and adding a new attribute, company, to detect to which company each post belongs. In the next subsections, each scenario is applied to the different classification algorithms, then a comparison between these algorithms according to some measurements such as precision, recall, and F- measures is done [23]. In order to apply the classification algorithms in RapidMiner, there are many steps should be performed, these steps will be briefly explained as follows:
• Read the datasets using Read Excel operator.
• Apply the process document operator in order to perform the preprocessing operations (tokenization, stop words filtering, and stemming). According to the experiments in [24], RapidMiner’s built-in preprocessing methods can be used for Jordanian delicts.
• Apply the cross-validation operator in order to split the datasets into training and testing datasets. The cross-validation operator splits the dataset into K folds and this value can be modified, (K-1) subsets are used to construct the model, and 1/K of the
subsets is used for testing the model. In each classification algorithm, we tried to find the best value of K before doing the comparison between them.
• In the scenarios of applying sampling on the datasets, the Sample operator is used with relative type and the "balance data" option is selected. By choosing "balance data" option, we can detect the ratio of each class label values, we chose 1% from negative examples and 100% from positive examples.
D. The Experiments Regarding the first scenario, the classification algorithms
are applied on the separate dataset for each company without using sampling. Naïve Bayes, SVM, and decision tree classifiers are applied to each company dataset with different values of K fold: 10, 15, 20, 25, 30, 35, and 40.The aim from varying K folds value is to detect the best value in term of the accuracy and F-measure then uses this values for comparison purposes between different classifiers. The KNN was also applied to the datasets of each company separately with varying the value of K: 1, 2, 3, 4, 5, 6 and 7, the value of K folds in these experiments was fixed to 10 which is the default value.
Since the data set for all companies are not balanced, a sampling approach was applied using the sample operator in the second experiment. In the third experiment, all companies datasets are merged in a dataset and applied all classifiers to the merged dataset.
E. Results and Discussion After the previous experiments were performed, the best
accuracy and f-measure values are computed based on k-folds value. Table 1, Table 2, and Table 3 show the results. The best accuracy and f-measure values are corresponding to k-folds or K value that provides the highest accuracy or f-measure value. Form the tables, it is clear that the SVM classifier outperforms all the other three classifiers in term of accuracy and F-measure for all datasets in all scenarios.
Regarding Orange dataset, in the first and second scenarios, the best accuracy and F-measure values were obtained when the K-folds value is equal to 35. Table 1 and Table 2 show that the best accuracy value in the first scenario is 92.51% and 82.95% in the second scenario, in another hand the best f-measure values are equal to 70.16% and 72.07% in the first and second scenario respectively.
Regarding Zain dataset, in the first and second scenarios, the best accuracy and F-measure values were obtained when the K-folds value is equal to 35. Table 1 and table 2 show that the best accuracy value in the first scenario is 92.51% and 82.95% in the second scenario, in another hand the best F-measure values are equal to 70.16% and 72.07% in the first and second scenario respectively.
Regarding Umniah dataset, in the first scenario, again when the K-folds value is equal to 35 it provides the best accuracy and F-measure values which are 96.11% and 54.73% respectively. In other hand, in the second scenario,
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the best accuracy value is gained when the K-folds value is equal to 15 with 88.04 %, and when the K-folds value is equal to 25. The best F-measure value is with 90.67%.
TABLE I. FIRST EXPERIMENT WITHOUT SAMPLING
Company Classifier Accuracy Precision Recall F- measure
Orange
SVM 92.51% 94.37% 55.24% 70.16% Naïve Bayes
80.35% 43.09% 43.33% 41.11%
Decision Tree
86.27% 61.76% 34.76% 44.68%
KNN 91.43% 87.03% 56.06% 66.59%
Zain
SVM 94.62% 94.48% 100% 97.13% Naïve Bayes
89.39% 93.83% 94.54% 94.17%
Decision Tree
91.18% 92.78% 97.96% 95.26%
KNN 94.34% 94.56% 99.51% 96.96%
Umniah
SVM 96.11% 90.16% 40.19% 54.73% Naïve Bayes
88.84% 23.73% 38.41% 29.02%
Decision Tree
94.01% 50% 17.17% 25.53%
KNN 94.7% 52.08% 49.31% 49.82%
TABLE II. SECOND EXPERIMENTS WITH SAMPLING.
Company Classifier Accuracy Precision Recall F- measure
Orange
SVM 82.95% 83.33% 60.48% 72.07% Naïve Bayes
68.67% 55.77% 45.71% 50.43%
Decision Tree
79.13% 74.51% 60.56% 66.67%
KNN 76.43% 83.83% 42.86% 54.65%
Zain
SVM 89.39% 90.02% 92.2% 89.85%
Naïve Bayes
77.59% 76.81% 82.83% 78.4%
Decision Tree
76.76% 73.68% 85.27% 78.57%
KNN 83.12% 85.27% 81.27% 82.56%
Umniah
SVM 88.04% 86.57% 96% 90.67%
Naïve Bayes
77.66% 79.2% 88.37% 82.87%
Decision Tree
81.9% 81.46% 92.21% 86.19%
KNN 84.37% 87.08% 87.66% 87.22%
Regarding the dataset which merged all datasets in one dataset in the third scenario, the best accuracy and F-measure values are 95.77% and 68.29% respectively which are obtained when the K-folds value is equal to 40 as shown in table 3.
IV. CONCLUSION AND FUTURE WORK Sentiment analysis of the users' posts on the Facebook
pages of the telecommunication companies becomes a useful method to analyze the user satisfaction for the provided services. In this paper, the users posts on the formal Facebook pages of the Jordanian telecommunication companies including Orange, Zain, and Umniah have been collected and classified manually, then four well-known classifiers: SVM, NB, Decision tree and KNN are applied to
compare between them and report which is the best in term of accuracy and f-measure in different scenarios The first scenario is to classify the dataset for each telecommunication company separately without sampling, the second scenario is to classify the dataset for each company with sampling, and the last scenario is to apply the different classifiers on the dataset with combining the datasets of all companies in a single dataset. In the first and second scenarios, the value of K-folds was varying from 10 to 40. For each scenario, a comparison between the different classifiers in term of F- measures values is provided. The results show that SVM classifier outperforms all other three classifiers in term of accuracy and F-measure for all datasets and all scenarios.
As a future work, it is more useful to detect if the user’s post is negative or positive by classifying the comments of these posts rather than depending only on the post itself.
TABLE III. THE BEST ACCURACY AND F-MEASURE VALUES IN THE THIRD SCENARIO
Classifier Accuracy Precision Recall F- measure SVM 95.77% 94.12% 55.57% 68.29%
Naive Bayes 78.5% 20.85% 56.49% 30.28%
Decision Tree
91.98% 63.93% 15.71% 24.88%
KNN 94.37% 75.31% 52.73% 61.7%
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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
