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AraCust: a Saudi Telecom Tweets corpus for sentiment analysis

Introduction

With the starting to be use of social media websites international over the remaining ten years, sentiment evaluation (SA) has lately turn into a favorite and positive technique for capturing public opinion in numerous disciplines. SA, or “opinion mining,” refers to a computational procedure of gathering people’ opinions, emotions, or attitudes against a specific experience or problem (Abdulla et al., 2014; Al-Thubaity et al., 2018). SA has a essential characteristic in precise-life applications and resolution-making procedures in quite a few domains (Al-Twairesh et al., 2017; Al-Twairesh & Al-Negheimish, 2019).

The detection of sentiment polarity, however, is a challenging task, due to obstacles of sentiment resources in distinctive languages. while a substantial body of analysis exists for English (Assiri, Emam & Al-Dossari, 2018; Al-Twairesh, 2016) it continues to be a largely unexplored analysis area for the Arabic language (Assiri, Emam & Al-Dossari, 2018; Al-Twairesh, 2016; Al-Twairesh et al., 2017; Habash, 2010), notwithstanding there is a big population of Arabic speakers (274 million international in 2019 Eberhard, Gary & Fennig (2021); 5th on this planet). here's due mainly to the complexity of Arabic (Habash, 2010; Al-Twairesh, 2016; Al-Twairesh et al., 2018b). It has many types, together with Classical Arabic (CA), as within the e-book of Islam’s Holy Quran, contemporary regular Arabic (MSA) utilized in newspapers, training, and formal speech, and Dialectical Arabic (DA), which is the informal well-known spoken language, found in chat rooms and social media structures. The Arabic language consists of 28 Arabic alphabet letters, eight of which are available in two kinds (Habash, 2010). Diacritics are used, that are small marks over or under letters positioned to replicate vowels. DA varieties differ from one Arab country to an extra. Mubarak & Darwish (2014) defined six Arabic dialects: Gulf, Yemeni, Iraqi, Egyptian, Levantine, and Maghrebi.

In 2020, Saudi Arabia reached 12 million Twitter clients (Statista, 2020). but for the Gulf dialect, certainly the Saudi dialect, fewer Saudi dialect corpus and lexicon components exist than for different Arabic dialects. for instance, the Egyptian dialect has had lots of attention, as has Levantine Arabic (Al-Twairesh, 2016). latest efforts have targeting the Gulf dialect (Khalifa et al., 2016a) and the Palestinian dialect (Jarrar et al., 2017), but components used for one Arabic country cannot be utilized to an extra. thus, there remains a necessity for Arabic corpora, together with DA (El-Khair, 2016); above all urgent is the should contain Saudi DA (Al-Twairesh, 2016).

there is additionally a scarcity of DA datasets and lexicons, particularly freely attainable GSC Saudi datasets (Assiri, Emam & Al-Dossari, 2018). sadly, the supply of the few latest resources is limited, due partially to strict procedures for gaining permission to reuse aggregated facts, with most current corpora now not providing free access. additionally, DA analysis, focused here, is advanced, requiring a native speaker.

finally, the telecom field has changed with the emergence of latest applied sciences. here is also the case with the telecom market in Saudi Arabia, which accelerated in 2003 via attracting new investors. as a result, the Saudi telecom market became a conceivable market (Al-Jazira, 2020).

This research goals to fill these gaps, by means of making a gold-common Saudi corpus AraCust and Saudi lexicon AraTweet to be used in records mining, particular to the telecom industry.

This paper’s main contributions are as follows. It makes a speciality of Arabic Sentiment analysis and offers options to one of the crucial challenges that faces Arabic SA with the aid of developing the biggest Saudi GSC. This resource is in response to statistics extracted from Twitter. it is additionally the primary corpus exceptionally centered to the telecom sector. It also gives an comparison of this corpus, extra demonstrating its nice and applicability.

First, we review connected analysis. Then, the methodology that became used in this research to construct the gold-typical annotation corpus is presented. next, it offers the corpus validation. finally, conclusions are drawn.

linked analysis

compared to other languages, Arabic lacks a large corpus (Assiri, Emam & Al-Dossari, 2018; Al-Twairesh, 2016; Al-Twairesh et al., 2017; Habash, 2010; Gamal et al., 2019). Many scholars have relied on translation from one language to an additional to assemble their corpus. as an instance, the Opinion Corpus for Arabic (OCA), one of the oldest and most-used corpora for ASA (Rushdi-Saleh et al., 2011), is created from more than 500 Arabic film reviews. The stories have been translated by using computerized laptop translation, and the results compared to each Arabic and English versions. in consequence, most research efforts have focused on enhancing classification accuracy with the OCA dataset (Atia & Shaalan, 2015). in addition, the MADAR (http://nlp.qatar.cmu.edu/madar/) corpus (Bouamor et al., 2018) covered 12,000 sentences from a basic touring Expression Corpus (BTEC) (Takezawa et al., 2007) and has been translated into French, MSA, and 25 Arabic dialects.

probably the most earliest Arabic datasets created as an MSA aid was the Penn Arabic Treebank (PATB) (Maamouri et al., 2004). It consisted of 350,000 words of newswire text and is available for a fee (https://catalog.ldc.upenn.edu/LDC2005T20). This dataset has been the main useful resource for some state-of-the-art methods and tools equivalent to MADA (Habash, Rambow & Roth, 2009), and its successor MADAMIRA (Pasha et al., 2014), and YAMAMA (Khalifa, Zalmout & Habash, 2016b).

Of the Arabic dialects, as mentions earlier than, the Egyptian dialect has had a wealth of consideration; the earliest Egyptian corpuses are CALLHOME (Gadalla et al., 1997; Gamal et al., 2019), and MIKA (Ibrahim, Abdou & Gheith, 2015). Levantine Arabic has additionally obtained a lot of attention, as within the advent of the Levantine Arabic Treebank (LATB) (Maamouri et al., 2006), including 27,000 words in Jordanian Arabic. Some efforts have been made for Tunisian (Masmoudi et al., 2014; Zribi et al., 2015), and Algerian (Smaıli et al., 2014). For Gulf Arabic, the Gumar corpus (Khalifa et al., 2016a) incorporates 1,200 documents written in Gulf Arabic dialects from distinct forum novels purchasable online (https://nyuad.nyu.edu/en/analysis/centers-labs-and-initiatives/computational-methods-to-modeling-language-lab/materials.html). the use of the Gumar corpus, a Morphological Corpus of the Emirati dialect changed into created (Khalifa et al., 2018), including 200,000 Emirati Arabic dialect words, which is freely available (https://nyuad.nyu.edu/en/analysis/centers-labs-and-initiatives/computational-methods-to-modeling-language-lab/materials.html). table 1 suggests extra details about the Arabic corpora. As may also be viewed, besides the above-mentioned, most of which can be freely purchasable, a good majority mentioned within the related literature are not or involve strict techniques for gaining permission to reuse aggregated statistics. moreover, most current corpora don't offer free entry.

desk 1:

assessment between diverse Arabic corpora.

Corpus nameRef. sourcelengthclass online availability Al-Hayat Corpus (De Roeck, 2002) Al-Hayat newspaper articles forty two,591 MSA attainable for a payment http://catalogue.elra.info/en-us/repository/browse/ELRA-W0030/ Arabic Lexicon for business commentsElhawary & Elfeky (2010) reviews2,000 URLs MSA now not attainable AWATIF (a multi-style corpus of contemporary commonplace Arabic) Abdul-Mageed & Diab (2012) Wikipedia talk Pages (WTP), The internet forum (WF) and half 1 V three.0 (ATB1V3) of the Penn Arabic TreeBank (PATB) 2855 sentences from PATB, 5,342 sentences from WTP and a pair of,532 sentences from WF MSA/Dialect no longer available The Arabic Opinion Holder Corpus Elarnaoty, AbdelRahman & Fahmy (000) information articles 1 MB news files MSA obtainable at http://altec-middle.org/ massive Arabic book review Corpus (LABR) Aly & Atiya (2013) booklet stories from GoodReads.com sixty three,257 booklet commentsMSA/Dialect Freely obtainable at http://www.mohamedaly.info/datasets Arabic Twitter Corpus (Refaee & Rieser, 2014) Twitter eight,868 tweets Arabic dialect attainable via the ELRA repository. An-Nahar Corpus Eckart et al. (2014) Newspaper text MSA obtainable for a fee https://catalog.elra.data/en-us/repository/browse/ELRA-W0027/ Tunisian Arabic Railway interplay Corpus (TARIC) (Masmoudi et al., 2014) Dialogues within the Tunisian Railway Transport community four,662 Tunisian dialect no longer available DARDASHA (Abdul-Mageed, Diab & okayübler, 2014) Chat Maktoob (Egyptian web site) 2,798 Arabic dialect now not obtainable TAGREED Twitter three,015 MSA/ Dialect TAHRIR Wikipedia talk pages 3,008 MSA MONTADA forums three,097 MSA/ Dialect hotel experiences (HTL) ElSahar & El-Beltagy (2014) TripAdvisor.com 15,572 MSA/ Dialect not attainable Restaurant stories (RES) Restaurant reviews (RES) from Qaym.com 10,970 MSA/ Dialect film reports (MOV) film stories (MOV) from Elcinemas.com 1,524 MSA/ Dialect Product reviews (PROD) Product experiences (PROD) from Souq.com four,272 MSA/ Dialect MIKA (Ibrahim, Abdou & Gheith, 2015) Twitter and different discussion board web sites for tv shows, product and lodge reservation. four,000 issues MSA and Egyptian dialect no longer accessible Arabic Sentiment Tweets Dataset (ASTD) (Nabil, Aly & Atiya, 2015) Twitter 10,000 Egyptian dialect. Egyptian dialect Freely obtainable at https://github.com/mahmoudnabil/ASTD fitness dataset (Alayba et al., 2017) Twitter 2026 tweets Arabic dialect not purchasable SUAR (Saudi corpus for NLP functions and substances) (Al-Twairesh et al., 2018a; Al-Twairesh et al., 2018b) distinctive social media sources such as Twitter, YouTube, Instagram and WhatsApp. 104,079 words Saudi dialect not attainable Twitter Benchmark Dataset for Arabic Sentiment evaluation (Gamal et al., 2019) Twitter 151,000 sentences MSA/ Egyptian dialect now not available

It is obvious from desk 2 that essentially the most-used supply for the Saudi corpus is Twitter. unluckily, none of the Saudi corpus is purchasable. furthermore, some of them do not mention details about the annotation, which might also pose a drawback for the usage of these corpora. This paper aims to fill this hole with the aid of offering the creation and annotation details about our GSC AraCust. additionally, we can make it freely purchasable to the analysis community. determine 1 illustrates the percentage of distinct Arabic corpus kinds. interestingly, we discovered that considering 2017, dialectal Arabic has been used in more corpora than MSA.

desk 2:

assessment between distinctive Saudi dialect corpora for ASA.

Corpus callRef. sourcesizecategoryOnline availability AraSenti-Tweet Corpus of Arabic SA (Al-Twairesh et al., 2017) Twitter 17,573 tweets fine, bad, neutral, or mixed labels. now not purchasable Saudi Dialects Twitter Corpus (SDTC) (Al-Thubaity et al., 2018) Twitter 5,400 tweets fine, poor, neutral, aim, spam, or no longer sure. now not attainable Sentiment corpus for Saudi dialect Alqarafi et al. (2018) Twitter 4,000 tweets high-quality or poor. not attainable Corpus for Sentiment analysis (Assiri, Emam & Al-Dossari, 2018) Twitter four,700 tweets no longer accessible Saudi public opinion Azmi & Alzanin (2014) Two Saudi newspapers 815 feedback Strongly advantageous, effective, bad, or strongly negative attainable upon request Saudi corpus Al-Harbi & Emam (2015) Twitter 5,500 tweets nice, negative, or impartialNot accessible Saudi corpus Al-Rubaiee, Qiu & Li (2016) Twitter 1,331 tweets nice, poor, or impartialNot purchasable figure 1: percent of Arabic corpora based on the type of corpus, from 2002 to 2019. records collection

To construct the dataset, we used Python to engage with Twitter’s search application programming interface (API) (Howard & Ruder, 2018) to fetch Arabic tweets in line with definite search keys. The Python language and its libraries are one of the vital bendy and regular approaches utilized in facts analytics, above all for desktop getting to know. To be sure pertinence to our goal software, we begun with hashtags related to the three largest Saudi telecom groups: the Saudi Telecom company (STC), the Etihad Etisalat business (Mobily), and Zain KSA, which dominate the market. because of this, we extracted the significant properly hashtags, as follows: #STC, #Mobily, #Zain, #,السعوديهand #, السعودية_ا that have been used for the hunt. These initial seed terms were extracted based on the following Python feature: tags = API.developments. place () from the tweepy library. moreover, we used the Twitter accounts of these agencies as search keywords.

because the purpose of this collection was to permit for a longitudinal, continual examine of telecom shoppers’ sentiments, we gathered statistics invariably from January to June 2017, in particular as a result of this duration contains purchasers’ reactions to the Saudi Communications and suggestions know-how fee’s new index, which refers to complaints submitted to the authorities (Saudi InformationTechnology fee, 2017). while apparently a brief duration, it definitely generated the biggest Arabic Telecom Twitter dataset for ASA. We had been mindful that we essential to account for the dataset as a result decreasing in size after spam and retweets had been eliminated. The preliminary outcome got comprised 3.5 million tweets. After filtering and cleaning (in line with area and time-zone and stratified random sampling; see below), the dataset became decreased to 795,500 Saudi tweets, which contain the significant AraCust dataset.

For our personal extra experimentations, as a way to in the reduction of computational prices and time in developing our working AraCust corpus, we chose a sub-pattern of Saudi tweets randomly from the dataset to avoid bias (Roberts & Torgerson, 1998). The fundamental suggestion at the back of the dimension reduction of the corpus become that the annotation method is manual, time-ingesting, and dear. exceptionally, to avoid bias within the sample, we applied right here steps: identify the population, specify the demo body, and decide the correct demo method. As brought up, the population during this look at is STC, Mobily and Zain customer tweets. The pattern frame is a Saudi tweet that describes the tweet author’s point of view concerning one of these agencies. The likelihood pattern technique is basic Random pattern (SRS), applied stratified over the three units (STC, Mobily, and Zain). The skills of SRS is that the entire inhabitants has the equal chance of being chosen (Marshall, 1996). furthermore, scholars have proven the efficiency of the random sampling method for social media, because gadgets that are repeated assorted times in the data set are more likely to seem often within the demo as neatly (Kim et al., 2018; Gerlitz & Rieder, 2013).

The pattern dimension resolution became according to a pattern-extraction test the usage of community Overview, Discovery, and Exploration Node XL (Smith et al., 2009). Node XL is an add-in tool for Microsoft Excel used in social media evaluation and visualization. as much as 2000 Arabic tweets have been retrieved the use of the prior to now mentioned hashtags. in accordance with the findings of an extra analyze that one hundred ten tweets per day are satisfactory to catch client sentiment (Assiri, Emam & Al-Dossari, 2018), we crucial 20,000 tweets over 6 months. moreover, we discovered that the capabilities provided with the aid of Saudi telecommunication agencies most frequently mentioned within the consumers’ tweets have been: internet pace, signal insurance, after-sales provider, name facilities, and fiber communication.

The dimension of our AraCust corpus of 20,000 Saudi tweets (table three) is in keeping with that of previous reviews, which showed that datasets over 20,000 tweets are adequate to supply state-of-the-art methods for Twitter Sentiment evaluation (SA) (Zhu, Kiritchenko & Mohammad, 2014; Mohammad, Kiritchenko & Zhu, 2013).

because the companies we focused were from Saudi Arabia, we additional filtered the tweets in keeping with consumer region and time zone to establish Saudi tweets. Saudi Arabia ranks seventh on earth within the number of personal accounts on social media (Arab news, 2020). We found that many tweets will not have a place field set in the profile of the clients who posted them. To resolve this concern, we used a listing of metropolis names, landmark names, city nicknames, and so forth., for Saudi Arabia, as additional labels for the user location of tweets, following Mubarak & Darwish (2014). also following Mubarak and Darwish, we used an inventory from the GeoNames web site (https://www.geonames.org/), a geographical database that includes 8 million region names for each and every nation, which contains 25,253 location names for Saudi Arabia.

finally, within the context of our statistics assortment system from Twitter, it is worth bringing up that ethical concerns of the use of social media facts have stirred an ongoing controversy in research communities in terms of confidentiality and privacy. the supply of social media statistics is thought to probably expose social media users to risks. besides the fact that children social media information is prominently public nevertheless, the emergence of profiling via enterprise house owners for business applications has led to criticism and apprehension. regarding our personal analyze, on Twitter, users’ mobilephone numbers and addresses aren't made public, to deliver some degree of privateness. additionally, in our current research, we further deleted any phone numbers or names that had been blanketed in the tweets themselves, for additional privateness. finally, we accrued simplest the tweet texts, time, and location, without accumulating another user-connected information from them.

desk three:

businesses and the whole number of enjoyable tweets from each and every in AraCust.

agencyTwitter address and hashtags # of enjoyable Tweets STC @STC_KSA, @STCcare, @STCLive 7,590 Mobily @Mobily, @Mobily1100, @MobilyBusiness 6,460 Zain @ZainKSA, @ZainHelpSA 5,950 total 20,000 Corpus cleansing and Pre-Processing

To keep away from noise within the corpus, cleaning become carried out on the dataset. a technique of cleaning is putting off junk mail, consequently any tweet with a Uniform useful resource Locator (URL) changed into excluded, as in Al-Twairesh (2016) and Alayba et al. (2017), because most tweets within the dataset with a URL had been news or junk mail. moreover, we excluded repetitive counsel, comparable to retweets, as advised with the aid of Barbosa & Feng (2010) and Alayba et al. (2017). additionally, non-Arabic tweets have been excluded from the information set by means of filtering for Arabic language (lang: AR), because translation damages the classifier efficiency. Pre-processing turned into achieved on the corpus using a Python script to eliminate pointless aspects in the tweets that could lessen accuracy from the tweet corpus before applying classifiers, similar to user mentions (@user), numbers, characters (corresponding to + = ∼$) and prevent words (such as “,”, “.”, “;”), as advised by way of Refaee & Rieser (2014) and Al-Twairesh (2016). The tweet corpus became processed using the natural Language Toolkit (NLTK) library in Python for normalization and tokenization. however emoticons could arguably specific sentiment, they have been deleted, as a result of prior research suggested a classifier misunderstanding between the parentheses in the quote and in the emoticon (Al-Twairesh, 2016). additionally, importantly, as we handled Arabic tweets, Refaee & Rieser (2014) confirmed that preserving emoticons in classification decreased the efficiency of the classifier; they cited that this changed into because of the manner Arabic sentences are written from correct-to-left, which is reversed in emoticons.

subsequent, the phrases in the tweets were tokenized, which capacity that sentences had been segmented into words for easier analysis, as in Al-Twairesh (2016) and sun, Luo & Chen (2017). finally, the tweets were normalized. For Arab textual content, normalization entails the unification of definite forms of Arabic letters of distinctive shapes, as in Al-Twairesh et al. (2017), i.e.:

  • changing the Arabic letters “أ“, ”إ”, and “أ“ with bare alif “أ“.

  • replacing the letter ”ئ“, ”ئ“, and ”ئ“ with naked ya ”ئ“.

  • replacing the final “ة” with “ة”.

  • If a notice starts with “ء”, replacing it with “أ“.

  • changing ”ؤ“ with ”ؤ“.

  • As stemming algorithms don't function neatly with DA phrases (Thelwall et al., 2011), they had been not applied. The records collection, filtering, cleansing, and pre-processing steps are illustrated in Fig. 2. The subset before and after pre-processing is illustrated in desk four. As proven in m, the emojis were deleted, and the prefix ““”ال “Al” was removed.

    figure 2: AraCust corpus assortment, filtering and pre-processing. desk 4:

    Subset of the corpus before and after pre-processing.

    Tweet in Arabic Label agencyTweet in English Tweet after pre-processing @So2019So @STCcare الشركهغيري bad STC alternate the business غيريشركه @alrakoo @mmshibani @GOclub @Mobily฀اشكرك positiveMobily thank you اشكرك @ZainKSA @CITC_withU يعوضنيعنالخسايرمين poor Zain Who will compensate me for the losses مينيعوضنيعنخساير Exploratory records analysis

    earlier than doing the sentiment analysis project, it's crucial to analyze the corpus. This contains the statistics varieties that we are going to contend with in the classification and prediction experiments, as smartly as the features that originate from the nature of the corpus, which may additionally have an effect on the mannequin’s efficiency. Our statistics analysis concerned many feature set analyses, from personality-based to dictionary-based, and syntactic aspects (Soler-enterprise & Wanner, 2018). This exploratory records analysis become accomplished the use of persona-primarily based, sentence-primarily based, and be aware-based features, to allow for processing at a number of ranges. The exploratory records evaluation changed into achieved the use of the NLTK library by way of a Python script.

    From the exploratory information evaluation, we followed first that there have been extra terrible tweets than high quality tweets for all three groups (see table 5 and Fig. three). We interpret this effect as being because of all Arab nations having suffered elaborate economic instances in the past few years; this result is in response to the findings by Refaee (2017) and Salameh, Mohammad & Kiritchenko (2015). subsequent, we analyzed the modifications between the tweet length distribution across the sentiment to examine no matter if there changed into some skills correlation there and since prior research used the tweet-length characteristic as enter to a computing device discovering classifier in SA analysis (Kiritchenko, Zhu & Mohammad, 2014; Al-Twairesh et al., 2018a) (Fig. 4). We followed that tweets are usually longer when customers express a poor sentiment. in addition, curiously, we found that STC clients had longer tweets overall than other organizations’ shoppers (Fig. 5). These effects guided us to use the All-Tweet length function within the classification assignment to estimate the influence of tweet size on the classifier’s efficiency.

    table 5:

    organizations and the overall number of high-quality and bad tweets.

    companyNegative effectiveTotal STC 5,065 2,525 7,590 Mobily 4,530 1,930 6,460 Zain 3,972 1,978 5,950 entire13,567 6,433 20,000 determine 3: Distribution of negative and tremendous sentiment. figure 4: Tweet length distribution across sentiment. figure 5: Tweet length distribution throughout companies.

    The ten most ordinary phrases in the corpus and their number of appearances within the corpus are given in table 6. It seems from the table that there is a repeated use of the note “God,” but just from this information we don't know even if it turned into repeated in a bad or fantastic manner. additionally, there became only one nice expression amongst these familiar words: “thanks” (which is one be aware in Arabic; see desk 6). The maximum frequency become, naturally, for the observe “cyber web,” which potentially shows the importance of this service; however likewise, we can not inform at this stage if the reason for having “information superhighway” among the most frequent phrases is high quality or bad. To superior be mindful the manner these words are used, we first studied the context of utilization by using the “most popular” bigram to provide a more comprehensive view of the statistics.

    table 6:

    Most common phrases within the AraCust corpus.

    notice in Arabic Frequency word in English نتا 1770 information superhighway لله 1760 God سلام 1363 hey والله 1179 Swear God خاص 1315 deepest حسبي 637 Pray عملاء 599 purchasers شكرا 560 thanks مشكلة 549 issue شريحة 515 Sim card

    probably the most popular bigram on the corpus, as proven in Fig. 6, is “pray” (be aware that here's expressed as two phrases in Arabic); here is in particular used in a terrible method, as defined under. Greetings are next in frequency, followed through “records sim card,” which we notion may also to be as a result of a popular problem source. We followed that cyber web carrier is described as gradual, so lots of the tweets that mentioned the internet are complaints, as shown beneath. moreover, “consumer provider” is likely one of the most everyday bigrams in the corpus.

    next, we calculated the positive and negative rate for each and every notice within the most frequent notice chart to assess even if the word become used with a good or terrible sentiment. We calculated the tremendous expense pr(t) and terrible fee nr(t) for probably the most universal words (time period t) in the corpus as follows (table 7):

    determine 6: Most regularly occurring Bigrams in the AraCust corpus. table 7:

    Most well-known words within the AraCust corpus and their sentiment likelihood.

    time period in Arabic term in English poor positiveTotal Pos_rate Neg_rate نت cyber web 975 795 1,770 0.forty four 0.fifty five الله God 977 783 1,760 0.44 0.55 سلام hi there 765 895 1,363 0.65 0.fifty six والله Swear God 567 704 1,179 0.59 0.48 خاص private 656 659 1,315 0.50 0.49 حسبي Pray 425 212 637 0.33 0.66 عملاء valued clientele 413 186 599 0.31 0.sixty eight شريحه Sim card 271 289 560 0.fifty one 0.forty eight مشكله challenge279 270 549 0.forty nine 0.50 شكرا thank you 235 280 515 0.fifty four 0.forty five

    p r t = t e r m _ f r e q _ d f t , ‘ p o s i t i v e ′ t e r m _ f r e q _ d f t n r t = t e r m _ f r e q _ d f t , ‘ n e g a t i v e ′ t e r m _ f r e q _ d f t where term_freq_df [t, val]; val ∈effective, negative; is the frequency of the word t as a observe with valence (sentiment) val in the corpus: t e r m _ f r e q _ d f t , v a l = ∑ t w ∈ C n bool 1 tw , t , val where tw is a tweet in corpus C; and bool1() is a Boolean characteristic: b o o l 1 t w , t , v a l = 1 , v a l e n c e t w , t = v a l 0 , r e s t With valence(tw, t) a function returning the sentiment of a note t in a tweet tw and term_freq_df [t] is the total frequency of the word t as both a favorable and terrible note in the corpus: t e r m _ f r e q _ d f t = ∑ t w ∈ C n bool 2 tw , t the place bool2() is a Boolean feature: b o o l 2 t w , t = 1 , t ∈ t w 0 , t ∉ t w We found that “cyber web” is used as a terrible word greater than a good notice, as we found out before. moreover, possibly noticeably, the observe “God” is used in terrible tweets more than in high quality ones. The words “good day,” “Swear to God,” “inner most,” “sim card,” and “thank you” are used as fine words more than as bad phrases (contrary to our initial supposition that the frequency of “sim card” may also indicate an issue). furthermore, we discovered the notice “valued clientele” used as a poor be aware more than a favorable notice.

    These effects led us to use the Has Prayer function within the classification task; this function allows for us to evaluate no matter if the existence of a prayer in a tweet raises the classifier’s performance.

    The characteristic set analysis is illustrated in Tables eight, 9 and 10. persona-based features (table eight) replicate the existence of symbols, comparable to a minus sign, punctuation marks corresponding to a comma, and numbers. The ratio turned into measured between the number of characters in a tweet and the variety of characters usual.

    desk 8:

    character-primarily based aspects.

    character-based mostly function Ratio Punctuation marks eight.0 Numbers 6.03 symbol 0.0 table 9:

    Sentence-based mostly features.

    Sentence-primarily based function Ratio words per sentence sixteen.23 Sentence standard deviation 7.17 diversity30 desk 10:

    notice-based mostly elements.

    observe-based mostly feature Ratio observe typical deviation 6.fifty one notice range30 Chars per notice 5.22 Vocabulary richness 1.0 stop phrases 0.0 suitable nouns 0.11

    be aware-based facets (desk 9) consist of be aware normal deviation, which was calculated the usage of the commonplace deviation of notice length, notice range (the difference between the longest and shortest be aware), characters per word calculated by way of the mean variety of characters for each notice, and vocabulary richness, which is the count of a considerable number of words.

    Sentence-primarily based features encompass the suggest number of phrases for each and every sentence, the commonplace deviation of sentence size, and range (the difference between the longest and shortest sentence) (table 10).

    Annotation

    before the SA, we vital to educate the classifier and create a readable edition for the computing device the use of corpus annotation. Annotation is the technique of assigning interpretative tips to a doc collection for mining use (Leech, 1993). Hinze et al. (2012) defined annotation as using predefined classes to mark the text, sentence, or words. Salameh, Mohammad & Kiritchenko (2015) defined annotation as providing the opinions and sentiments against a target. There are distinctive ranges of corpus annotation. as an example, sentiment annotation and syntactic annotation is the technique of parsing each sentence within the corpus and labeling it with its constitution, grammar, and part-of-speech (POS)—it's, labeling every word within the corpus with a corresponding acceptable POS label.

    several procedures used to annotate the corpus, together with the manual method, which depends upon human labor, and the computerized approach, which makes use of an annotation tool.

    Gold usual Corpora (GSC) are a crucial requirement for the development of computing device studying classifiers for natural language processing with effectivity; besides the fact that children, they are costly and time ingesting and hence there are few GSCs available, chiefly for Arabic (Wissler et al., 2014).

    The method of development of the GSC is in line with manual annotation through diverse consultants who review the statistics for my part, and then inter-annotator settlement is computed to verify the best (Wissler et al., 2014).

    For sentiment annotation, a couple of reviews used three-approach classification labels (fine, bad and neutral) to categorical sentiment orientation (Abbasi, Chen & Salem, 2008; Refaee & Rieser, 2014; Refaee & Rieser, 2016; Al-Twairesh, 2016). The output from the classification is in line with the labels used within the annotation. during this analysis, we classified the corpora the use of binary classification (terrible vs. effective) to foretell consumer delight toward the telecom company, following many experiences that used binary sentiment classification with Arabic text (Mourad & Darwish, 2013; Refaee & Rieser, 2016; Al-Twairesh, 2016; Abdul-Mageed, Diab & okübler, 2014). a couple of prior stories have shown that binary classification is extra accurate than other classifications (Refaee & Rieser, 2016; Al-Twairesh, 2016). each and every sentiment label is a binary measure of consumer delight: “convinced” and “unhappy.”

    Sarcasm is a kind of speech in which an individual says something fantastic while he/she in fact skill whatever thing terrible, or vice versa (Liu, 2015). Sarcasm is notoriously hard to observe; in English, there are only a number of reviews on sarcasm detection the usage of supervised and semi-supervised gaining knowledge of procedures (Liu, 2015). There were no reports that have taken on sarcasm detection in ASA. for this reason, we asked the annotators to additionally label tweets with the presence of sarcasm, based on the sentiment they conveyed. This allowed us to be able to use sarcasm as a function for computer studying classification, following Refaee & Rieser (2016). We for this reason opened the way for the primary sarcasm-detection Arabic NLP work.

    The corpora had been divided into three corpora, in response to the telecom business because the key phrase (STC, Mobily, Zain). To make certain a top quality of the guide annotation method, clear guidelines had been mandatory to preserve consistency between annotators (Al-Twairesh, 2016).

    As counseled by using Alayba et al. (2017) and Al-Twairesh (2016), three annotators have been employed in this analysis to annotate our corpus. Our annotators, A1, A2, and A3, had been all computer science graduates, native speakers of the Saudi dialect, and had prior annotation journey. The cause of settling on three annotators as an alternative of the standard, and less demanding, two, was to increase the excellent of the resulting corpus by means of assuaging conflicts that might come up from discrepancies between only two annotators. therefore, if two annotators disagreed with respect to at least one tweet classification, we took a vote between all three annotators. furthermore, Pustejovsky & Stubbs (2012) stated that greater than two annotators is preferable.

    To inspire a radical examination of the tweets and remarkable outcomes, the annotators were paid. additionally, to make certain reasonable pay, so as to check the annotators’ wages, we carried out a pilot analyze to calculate the normal time they essential to annotate the tweets, as recommended by using Al-Twairesh (2016). We offered the annotators with a hundred and ten tweets (Assiri, Emam & Al-Dossari, 2018) and the annotation guideline, and then calculated the regular time that they obligatory for annotation. They took 33 min, 20 min, and 35 min to annotate the one hundred ten tweets. thus, the average time that they vital changed into 30 min to annotate 110 tweets. We then paid them to annotate the 20,000 tweets over the path of 2.5 months, two hours per day for five workdays per week.

    before we began the annotation method, the annotators had been provided with annotation instructions in each Arabic and English in a one-hour session; some of the annotation guidelines are proven in desk eleven. We stored the annotations in an Excel file. The annotation guidelines had been also included within the Excel file in case the annotator vital to read it (Fig. 7). As suggested with the aid of Pustejovsky & Stubbs (2012), we constructed a straightforward interface within the Excel file that has the tweets, an automatic list box of labels to keep away from typing errors, the sentiment-bearing words, and the telecom functions outlined within the tweet, if discovered (Fig. eight).

    To build a gold average Arabic corpus, three rotations have been used to annotate the corpus. As outlined before, we divided the corpora into three based on the Telecom agencies STC, Mobily, and Zain. They began the primary rotation with the aid of annotating the STC corpus, then the Mobily corpus, adopted by means of the Zain corpus. After the primary rotation, we reviewed the annotators’ decisions and discussed them with them before the brand new rotation all started. After the 2nd rotation, we calculated the similarity percent between A1 and A2, A2 and A3, and A1 and A3 for the three corpora. on the third rotation, we asked the annotators to revise the labels for the corpus that have low similarity percentages. After the three rotations, the writer revised the three annotation labels achieved by way of the annotators and in comparison their choices, the use of balloting to make choices. We found that eighty three% of the tweets have been labeled with the same label by means of the A1 and A3, seventy five% of the tweets were categorized with the equal labels via A2 and A3, while seventy four% of the tweets have been categorized via A1 and A2 with the identical labels.

    figure 7: The covered annotation guidelines in the XLSX file. figure 8: The annotation file. Annotation Challenges

    The annotators confronted some challenges in the annotation technique, similar to these skilled in prior research (Cambria et al., 2017), equivalent to:

    •  Quoting and supplications: it's complicated to outline the sentiment of a tweet writer whose tweet includes a quote or supplication, and to check no matter if the author concurs with the sentiment of the quoted author. The annotators selected the sentiment that changed into expressed in the quote or within the supplication. Then, we checked the sentiment that they allotted. We didn't ignore or eradicate the tweets with prices or supplications, since the costs/supplications have been a kind of expression of author sentiment.

    •  Sarcasm: it's extraordinarily complicated to detect sarcasm in a tweet, because the explicit sentiment is distinctive from the implicit sentiment. in spite of this, as people are superior at this than machines, annotation of tweets with this label is useful due to the problem of the sarcasm detection assignment (Rajadesingan, Zafarani & Liu, 2015). For that, we requested them to label a tweet for that reason if they might discover sarcasm in it.

    •  Defining the telecom services on the tweet: The annotators indicated that no longer all the tweets mentioned telecom features. This could be associated with the character of the tweet, which is short. for that reason, we requested annotators to define the telecom capabilities in the event that they discovered them in the tweet.

    •  Absence of diacritics: this makes the pronunciation of a notice difficult, because with out diacritical marks, some words have two possible meanings. For these, we requested the annotators to interpret the notice in the context of its sentence.

    Inter-annotator contract

    To identify the reliability of the annotation scheme, the inter-annotator settlement (IAA) became used. We used the similarity index as an early indicator of the annotators’ agreement. Fleiss’ Kappa (Davies & Fleiss, 1982) turned into used to measure consistency for the 5-approach classification (enormously effective, fine, neutral, terrible, incredibly terrible) and for the binary classification (effective, terrible), because there were greater than two annotators (Davies & Fleiss, 1982; Fleiss, 1971).

    The kappa k Fleiss (Fleiss, 1971) is defined as: ok = P ¯ − P e ¯ 1 − p e ¯ the place P e ¯ expresses the normalization of the settlement it truly is purchasable randomly and P ¯ offers the normalized chance of contract achieved accidentally. If the annotators are in finished contract, then k = 1. If there isn't any agreement among the many annotators, then okay <  0. The price we acquired was of 0.50 for five-manner classification and nil.60 for binary classification for the three annotators, which is a moderate stage in line with the level of acceptance (Landis & Koch, 1977). in addition, we checked for agreement two-by way of-two between A1 and A2, A1 and A3, and A2 and A3, and we took the typical A (desk 12).

    desk 12:

    Two-with the aid of-two agreement for binary classification between the three annotators.

    Annotators k A1& A2 0.7 A2 & A3 0.seventy four A1 & A3 0.87 Avg A 0.seventy seven comparison of the Corpus

    To evaluate our AraCust corpus, we utilized an easy scan the usage of a supervised classifier to offer benchmark results for drawing close works. additionally, we applied the identical supervised classifier on a publicly available Arabic dataset produced from Twitter, ASTD (Nabil, Aly & Atiya, 2015), to compare the consequences of AraCust and ASTD; the details of these datasets are provided in desk 13. We used a guide Vector computer (SVM), which has been used in Arabic sentiment analysis in fresh analysis with excessive accuracy (Mubarak et al., 2020; Alayba et al., 2017; Bahassine et al., 2020). We used a binary classification (positive, bad) and eradicated tweets with distinct classification labels from the ASTD records set. We used a linear kernel with an SVM classifier, as some stories have brought up that here is the top-rated kernel for textual content classification (Mohammad, Kiritchenko & Zhu, 2013; Al-Twairesh et al., 2017; Refaee & Rieser, 2016). The AraCust and ASTD corpora have been split right into a practicing set and look at various set; moreover, 10-fold pass-validation became carried out for each to achieve the top-rated error estimate (James et al., 2013). For oversampling because of the dataset being biased in opposition t poor tweets, we used the usual artificial Minority Over-Sampling technique (SMOTE). The findings are in the test set, table 14.

    table 13:

    Datasets used within the evaluation.

    data Set effective tweets terrible tweets completeAracust 6,433 13,567 20,000 ASTD 797 1,682 2,479 desk 14:

    contrast results of the use of the SVM on the datasets.

    data Set valuablepoor complete Precision recall F1 Precision keep in mind F1 F1 avg Accuracy Aracust ninety three.0 seventy six.0 83.6 91.0 98.0 94.4 89.0 ninety one.0 ASTD 79.0 sixty five.0 71.3 seventy six.0 ninety six.0 84.four 77.9eighty five.0

    We analyzed the elements term presence, term frequency (TF) (the frequency of each time period within the doc), and term frequency–inverse document frequency (TF–IDF) (the frequency of each word based on all records’ frequencies). We discovered that time period presence is the most desirable function to make use of with binary classification, in line with what turned into found with the aid of Al-Twairesh et al. (2018a), which is that term presence is finest for binary classification due to a lack of time period repetition within a brief textual content, similar to a tweet. moreover, Forman (2003) stated that a time period presence mannequin can deliver information comparable to time period frequency for brief texts. Pang & Lee (2008) mentioned that the usage of time period presence results in more advantageous efficiency than using term frequency. The results in table 14 exhibit that our dataset AraCust outperforms the ASTD influence. additional research may additionally investigate the usage of deep getting to know algorithms on our newly created GSC AraCust dataset.

    study Validation

    This analyze used a sentiment analysis on GSC AraCust to measure client satisfaction. To validate the proposed method, we developed an easy questionnaire of two questions. The questionnaire is oriented towards the purchasers whose tweets were mined, to compare the estimated customer pride using the proposed approach with genuine customer delight the usage of the questionnaire (table 15).

    table 15:

    percentage of expected consumers satisfaction vs. genuine customer’s pride.

    agencyPredicted customer’s satisfaction precise client’s pride STC forty.01% 20.1% Mobily 39.00% 22.89% Zain 34.06% 22.ninety one%

    We made an automated tweet generator in Python (the tweet has a hyperlink to the questionnaire) to all 20,000 clients whose tweets we had prior to now mined, however the respondents totaled simply 200. The tweet generator was created using a code in Python for sending tweets that have two things (the hyperlink to the questionnaire and mentions to the Twitter accounts of contributors). To shop time, the code completed this method immediately (Fig. 9). The questionnaire changed into inbuilt Google varieties because it is convenient to construct and distribute. The questions have been: “what is your telecom business?” and “outline your satisfaction toward your company (satisfied, unhappy).” We obtained 530 responses. The pattern turned into disbursed between purchasers of the three groups, as shown in Fig. 10.

    determine 9: picture from the Python code for tweets generator. figure 10: number of participants in keeping with telecom organizations. figure eleven: variety of satisfied and unsatisfied users for STC company. determine 12: number of convinced and unhappy clients for Mobily business. determine 13: variety of satisfied and unsatisfied users for Zain enterprise.

    The unbalanced numbers of individuals between the three companies displays the precise distribution of the clients of the Saudi telecom organizations. The variety of unsatisfied and convinced users for STC is proven in Fig. 11, for Mobily in Fig. 12, and for Zain in Fig. 13.

    desk 15 indicates that the proposed strategy carried out the aim of predicting client pride of telecom businesses in response to the Twitter evaluation.

    These effects can deliver insights for the determination-makers in these groups involving the percent of client pride and help to enhance the capabilities offered by means of these companies. These consequences may still inspire determination-makers to agree with using Twitter analyses for measuring client pride and to include it as a brand new method for evaluating their marketing recommendations.

    Conclusion

    This look at got down to fill gaps within the literature by way of proposing the greatest gold-common corpus of Saudi tweets created for ASA. it is freely attainable to the research community. This paper described in element the advent and pre-processing of our GSC AraCust, explained the annotation steps that have been adopted in creating AraCust, and described elements of the corpus, which consists of 20,000 Saudi tweets. A baseline scan was utilized on AraCust to offer benchmark results for forthcoming works. additionally, a baseline scan turned into utilized to ASTD to examine the effects with AraCust. The outcomes reveal that AraCust is advanced to ASTD. further generalization of the dataset use can look into other features of the communications of purchasers of the three majors Saudi providers of telecom features—serving, as an example, a complete of 41.sixty three million subscribers who use cellular voice verbal exchange capabilities. furthermore, we've counseled the telecom carrier corporations of our consequences at every step of our investigation, and these outcomes, dataset, and ordinary methodology may be used in the future to enhance their services for his or her purchasers.

    Supplemental information Python code for corpus preprocessing and exploratory records analysis

    References


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