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            <front>

                <journal-meta>
                                                                <journal-id>jobda</journal-id>
            <journal-title-group>
                                                                                    <journal-title>Journal of Business in The Digital Age</journal-title>
            </journal-title-group>
                                        <issn pub-type="epub">2651-4737</issn>
                                                                                            <publisher>
                    <publisher-name>Berrin ONARAN</publisher-name>
                </publisher>
                    </journal-meta>
                <article-meta>
                                        <article-id pub-id-type="doi">10.46238/jobda.1121697</article-id>
                                                                <article-categories>
                                            <subj-group  xml:lang="en">
                                                            <subject>Business Administration</subject>
                                                    </subj-group>
                                            <subj-group  xml:lang="tr">
                                                            <subject>İşletme </subject>
                                                    </subj-group>
                                    </article-categories>
                                                                                                                                                        <title-group>
                                                                                                                        <trans-title-group xml:lang="en">
                                    <trans-title>POSITIONING IN HEAVY COMMERCIAL VEHICLE BRANDS: CREATING PERCEPTION MAPS BY TEXT MINING METHOD</trans-title>
                                </trans-title-group>
                                                                                                                                                                                                <article-title>AĞIR TİCARİ ARAÇ MARKALARINDA KONUMLANDIRMA: METİN MADENCİLİĞİ YÖNTEMİ İLE ALGILAMA HARİTALARININ OLUŞTURULMASI</article-title>
                                                                                                    </title-group>
            
                                                    <contrib-group content-type="authors">
                                                                        <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0003-0567-1404</contrib-id>
                                                                <name>
                                    <surname>Memiş Kamacı</surname>
                                    <given-names>Hilal</given-names>
                                </name>
                                                                    <aff>SAMSUN ÜNİVERSİTESİ</aff>
                                                            </contrib>
                                                    <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0002-6906-5414</contrib-id>
                                                                <name>
                                    <surname>Yılmaz</surname>
                                    <given-names>Mustafa Kemal</given-names>
                                </name>
                                                                    <aff>SAMSUN ÜNİVERSİTESİ</aff>
                                                            </contrib>
                                                                                </contrib-group>
                        
                                        <pub-date pub-type="pub" iso-8601-date="20221231">
                    <day>12</day>
                    <month>31</month>
                    <year>2022</year>
                </pub-date>
                                        <volume>5</volume>
                                        <issue>2</issue>
                                        <fpage>161</fpage>
                                        <lpage>179</lpage>
                        
                        <history>
                                    <date date-type="received" iso-8601-date="20220526">
                        <day>05</day>
                        <month>26</month>
                        <year>2022</year>
                    </date>
                                                    <date date-type="accepted" iso-8601-date="20221228">
                        <day>12</day>
                        <month>28</month>
                        <year>2022</year>
                    </date>
                            </history>
                                        <permissions>
                    <copyright-statement>Copyright © 2018, Journal of Business in The Digital Age</copyright-statement>
                    <copyright-year>2018</copyright-year>
                    <copyright-holder>Journal of Business in The Digital Age</copyright-holder>
                </permissions>
            
                                                                                                <trans-abstract xml:lang="en">
                            <p>In this study, perception maps of Mercedes Benz, Scania, BMC, Ford Trucks, Daf, Iveco, Man, Volvo and Renault Trucks brands, which are the main actors of the heavy commercial vehicle segment in Turkey according to TAID (Heavy Commercial Vehicles Association) data, were created by text mining method. The textual data needed in the study carried out using the text mining method; it was obtained from the comments made on the content produced for the relevant brands on YouTube, the social media platform that is the most preferred by content producers and advertisers and where user comments are the most. Considering the number of views in the content included in the study; a total of 126 videos and 19407 viewer comments were evaluated for nine different heavy commercial vehicle scissors. According to the results of the analysis, the words frequently used when evaluating brands or products in audience comments; quality, poor quality, good, bad, local, cheap, expensive, mashallah, congratulations, magnificent and halal expressions. Relative frequency values were used to determine the densities of the words detected in the comments made on the content produced for each brand and to create graphical presentations of the user/audience/consumer perception maps.</p></trans-abstract>
                                                                                                                                    <abstract><p>Bu çalışmada, TAİD (Ağır Ticari Araçlar Derneği) verilerine göre Türkiye’de ağır ticari araç segmentinde sektörün başlıca aktörleri olan Mercedes-Benz, Scania, BMC, Ford Trucks, Daf, Iveco, Man, Volvo ve Renault Trucks markalarının metin madenciliği yöntemiyle algılama haritaları oluşturulmuştur. Metin madenciliği yönteminden faydalanılarak yürütülen çalışmada ihtiyaç duyulan metinsel veriler; içerik üretenler ve reklam verenlerin en çok tercih ettiği ve kullanıcı yorumlarının en çok yer aldığı sosyal medya platformu olan YouTube’da ilgili markalar için üretilen içeriklere yapılan yorumlardan elde edilmiştir. Çalışmaya dâhil edilen içeriklerde izlenme sayıları dikkate alınarak; 9 farklı ağır ticari araç makası için toplam 126 adet video ve 19407 adet izleyici yorumu değerlendirilmiştir. Analiz sonuçlarına göre, izleyici yorumlarında markaları değerlendirirken sıklıkla kullanılan kelimeler; kaliteli, kalitesiz, iyi, kötü, yerli, ucuz, pahalı, maşallah, tebrikler, muhteşem ve helal olsun ifadeleri olarak belirlenmiştir. Her bir markaya yönelik üretilen içeriklere yapılan yorumlarda tespit edilen kelimelerin yoğunluklarını belirleyebilmek ve kullanıcı/izleyici/tüketici algılama haritalarının grafiksel sunumlarını oluşturabilmek için relatif frekans değerlerinden faydalanılmıştır.</p></abstract>
                                                            
            
                                                                                        <kwd-group>
                                                    <kwd>Marka Konumlandırma</kwd>
                                                    <kwd>  Algılama Haritaları</kwd>
                                                    <kwd>  Metin Medenciliği</kwd>
                                                    <kwd>  Ağır Ticari Araç</kwd>
                                                    <kwd>  Relatif Frekans</kwd>
                                            </kwd-group>
                            
                                                <kwd-group xml:lang="en">
                                                    <kwd>Brand Positioning</kwd>
                                                    <kwd>  Perception Maps</kwd>
                                                    <kwd>  Text Mining</kwd>
                                                    <kwd>  Heavy Commercial Vehicle</kwd>
                                                    <kwd>  Relative Frequency</kwd>
                                                    <kwd>  Marka Konumlandırma</kwd>
                                                    <kwd>  Algılama Haritaları</kwd>
                                                    <kwd>  Metin Medenciliği</kwd>
                                                    <kwd>  Ağır Ticari Araç</kwd>
                                                    <kwd>  Relatif Frekans</kwd>
                                            </kwd-group>
                                                                                                                                        </article-meta>
    </front>
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