SOSYAL MEDYA ANALİTİĞİ İLE DEĞER YARATMA: DUYGU ANALİZİ İLE GELECEĞE YÖNELİM

Year 2018, Volume: 5 Issue: 3, 797 - 811, 27.12.2018

Burcu Akın , Umman Tuğba Gürsoy Şimşek

https://doi.org/10.30798/makuiibf.435804

Cited By: 9

Abstract

Müşterisi
sosyal medya kullanıcıları olan işletmelerin bu kanallardan ürünleri hakkında
yazılan iletileri toplayıp analiz etme, bunları satış rakamları ile
karşılaştırma veya ilişkilendirme, gelecek için stratejiler geliştirme gibi
imkanları vardır. Bu çalışmada uygulanan yöntem ile bir televizyon kanalına ait
seyirci görüşlerinin sosyal medya üzerinden derlenerek kanal için faydalı bilgi
elde edilmesi ve bu bilgilerin kanal için bir karar destek unsuru olması
hedeflenmiştir. Bu amaçla bir TV kanalında yayınlanan programlar hakkında Kasım
- Haziran 2017 tarihleri arasında Twitter’dan paylaşılan iletiler derlenmiş, duygu
analizi tekniği ile her ileti pozitif, negatif ya da nötr olarak
etiketlenmiştir. Bu periyoddaki yayın akışındaki tüm programlar için kaç
pozitif, kaç negatif ve kaç nötr tweet atıldığı bilgilerine bağlı olarak
reyting değeri incelenmiştir.

Keywords

Duygu analizi, sosyal medya analitiği, regresyon analizi

SOCIAL MEDIA ANALYTICS: VALUE CREATION WITH SENTIMENT ANALYSIS

Abstract

Businesses with customers who are users of social media, have the
option of collecting and analyzing data from social media, comparing or
correlating them with sales figures, and developing strategies for the future.
By using the method applied in this study, it is aimed to obtain beneficial
information for the channel by compiling the views of spectators belonging to a
television channel through social media. Also, the study will provide a
decision support tool for the channel. For this purpose, Twitter is chosen as
social media channel and tweets which are about programs broadcast on a TV
channel, were compiled from November 2016 to June 2017. Each tweet is labeled
as positive, negative, or neutral by a novel sentiment analysis method. Then, for
all programs in this period, the rating was examined based on positive,
negative and neutral tweets. 

Keywords

Sentiment analysis, social media analytics, regression analysis

References

• AGRAWAL, D. Et Al. (2011) Challenges And Opportunities With Big Data.
• AHKTER, J. K. VE SORIA, S. (2010) Sentiment Analysis: Facebook Status Messages. Unpublished Master's Thesis, Stanford, Ca.
• ARAMAKI, E. VE MASKAWA, S., MORITA, M. (2011) Twitter Catches The Flu: Detecting Influenza Epidemics Using Twitter In: Proceedings Of The Conference On Empirical Methods In Natural Language Processing. Association For Computational Linguistics, P. 1568-1576.
• ARDIÇ, B. VE GÖKTÜRK, M. (2009) Kullanilabilir Uygulama Programlama Arayüzleri 4. Ulusal Yazilim Mühendisliği Sempozyumu, Beşiktaş, İstanbul, 91-97.
• ASUR, S. VE HUBERMAN, B. A. (2010) Predicting The Future With Social Media In: Web Intelligence And Intelligent Agent Technology (Wi-Iat), Ieee/Wic/Acm International Conference On. Ieee, P. 492-499.
• BALOĞLU, A. (2015) Sosyal Medya Madenciliği, Beta Yayınları.
• BATRINCA, B. ve TRELEAVEN, P. C. (2015) Social Media Analytics: A Survey Of Techniques Tools And Platforms. Ai & Society, 30.1: 89-116.
• BELLO-ORGAZ, G., JUNG, JASON J. ve CAMACHO, D. (2016) Social Big Data: Recent Achievements And New Challenges Information Fusion, 28: 45-59.
• BODNAR, T. ve SALATHÉ, M. (2013) Validating Models For Disease Detection Using Twitter In: Proceedings Of The 22nd International Conference On World Wide Web. Acm, P. 699-702.
• CIOFFI-REVILLA, C. (2010) Wiley Interdisciplinary Reviews: Computational Statistics Vol. 2, No. 3, Pp. 259-271.
• CODAL, K. S. ve COŞKUN, E. (2016) Sosyal Ağ Türlerinin Karşilaştirilmasina İlişkin Bir Ağ Analizi Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi.
• CULOTTA, A. (2010) Towards Detecting İnfluenza Epidemics By Analyzing Twitter Messages In: Proceedings Of The First Workshop On Social Media Analytics. Acm, P. 115-122.
• CVIJIKJ, I. P. VE MICHAHELLES, F. (2013) Online Engagement Factors On Facebook Brand Pages Social Network Analysis And Mining, 3.4: 843-861.
• ÇOBAN, Ö., ÖZYER, B. VE ÖZYER, G. T. (2015) Sentiment Analysis For Turkish Twitter Feeds In: Signal Processing And Communications Applications Conference (Siu), 23th. Ieee, 2015. P. 2388-2391.
• DING, C., Et Al. (2017) The Power Of The Like Button: The Impact Of Social Media On Box Office Decision Support Systems, 94: 77-84.
• EIRINAKI, M., PISAL, S. VE SINGH, J. (2012) Feature-Based Opinion Mining And Ranking Journal Of Computer And System Sciences, 78.4: 1175-1184.
• GHIASSI, M., SKINNER, J. VE ZIMBRA, D. (2013) Twitter Brand Sentiment Analysis: A Hybrid System Using N-Gram Analysis And Dynamic Artificial Neural Network Expert Systems With Applications, 40.16: 6266-6282.
• GUNAWARDENA, N. Et Al. (2013) Instagram Hashtag Sentiment Analysis In: University Of Utah Cs530/Cs630 Conference Of Machine Learning.
• GÜRSOY, U. T. VE BILGIN, S. (2016) Banka Müsterilerinin Internet Bankaciligina Iliskin Yaklasimlarinin Veri Madenciligi Teknikleri Ile Incelenmesi Kafkas University. Faculty Of Economics And Administrative Sciences. Journal, 7.14: 421.
• HANNEMAN, R. A. VE RIDDLE, M. (2005) Introduction To Social Network Methods.
• HANSEN, D., SHNEIDERMAN, B. ve SMITH, M. A. (2010) Analyzing Social Media Networks With Nodexl: Insights From A Connected World. Morgan Kaufmann.
• HE, W., ZHA, S. VE LI, L. (2013) Social Media Competitive Analysis And Text Mining: A Case Study In The Pizza Industry International Journal Of Information Management, 33.3: 464-472.
• HUR, M., KANG, P. VE CHO, S. (2016) Box-Office Forecasting Based On Sentiments Of Movie Reviews And Independent Subspace Method Information Sciences, 372: 608-624.
• JANG, H. Et Al. (2013) Deep Sentiment Analysis: Mining The Causality Between Personality-Value-Attitude For Analyzing Business Ads In Social Media Expert Systems With Applications, 40.18: 7492-7503.
• JANSEN, B. J. Et Al. (2009) Twitter Power: Tweets As Electronic Word Of Mouth Journal Of The American Society For İnformation Science And Technology, 60.11: 2169-2188.
• JOSHI, M. Et Al. (2010) Movie Reviews And Revenues: An Experiment In Text Regression In: Human Language Technologies: The 2010 Annual Conference Of The North American Chapter Of The Association For Computational Linguistics. Association For Computational Linguistics, P. 293-296.
• KANG, D. ve PARK, Y. (2014) Review-Based Measurement Of Customer Satisfaction In Mobile Service: Sentiment Analysis And Vikor Approach Expert Systems With Applications, 41.4: 1041-1050.
• KAPLAN, A. M. ve HAENLEIN, M. (2010) Users Of The World, Unite! The Challenges And Opportunities Of Social Media Business Horizons, 53.1: 59-68.
• KARAÖZ B. (2018) Büyük Veri Ve İşletme Analitiği: Sosyal Medya Ve Duygu Analizi Ile Bir Öngörü Modeli Yayınlanmamış Doktora Tezi, İstanbul, İ.Ü. Sosyal Bilimler Enstitüsü.
• KDNUGGETS (2017) www.kdnuggets.com
• KIETZMANN, J. H. Et Al. (2011) Social Media? Get Serious! Understanding The Functional Building Blocks Of Social Media Business Horizons, 54.3: 241-251.
• KIM, T., HONG, J. VE KANG, P. (2015) Box Office Forecasting Using Machine Learning Algorithms Based On Sns Data International Journal Of Forecasting, 31.2: 364-390.
• MERAL, M. VE DIRI, B. (2014) Sentiment Analysis On Twitter In: Signal Processing And Communications Applications Conference (Siu), 22nd. Ieee, 2014. 690-693.
• MISHNE, G. Et Al. (2006) Predicting Movie Sales From Blogger Sentiment In: Aaai Spring Symposium: Computational Approaches To Analyzing Weblogs, P. 155-158.
• MOSTAFA, M. M. (2013) More Than Words: Social Networks’ Text Mining For Consumer Brand Sentiments Expert Systems With Applications, 40.10: 4241-4251.
• MOORE, L. (2014) Is Your Advertising Campaign Driving Intent To Purchase?
• NEMSCHOFF, M. (2013) Social Media Marketing: How Big Data Is Changing Everything Cms Wire, 16.
• ÖZDAĞOĞLU, G., KAPUCUGIL-İKIZ, A. VE ÇELIK, A. F. (2016) Topic Modelling-Based Decision Framework For Analysing Digital Voice Of The Customer Total Quality Management & Business Excellence, 1-18.
• RUI, H., LIU, Y. VE WHINSTON, A. (2013) Whose And What Chatter Matters? The Effect Of Tweets On Movie Sales Decision Support Systems, 55.4: 863-870.
• SCHLAGWEIN, D. (2014) Strategic Tools: How Firms Successfully Use Social Media From Http://Www.Smartcompany.Com.Au/Leadership/Management/41115‐ Strategic‐Tools‐How‐Firms‐Successfully‐Use‐Social‐Media.Html.
• SHARDA, R. VE DELEN, D. (2006) Predicting Box-Office Success Of Motion Pictures With Neural Networks Expert Systems With Applications, 30.2: 243-254.
• THELWALL, M. (2017) Social Media Analytics For Youtube Comments: Potential And Limitations International Journal Of Social Research Methodology. 1-14.
• TRATTNER, C. VE KAPPE, F. (2013) Social Stream Marketing On Facebook: A Case Study International Journal Of Social And Humanistic Computing, 2.1-2: 86-103.WAMBA, S. F., AKTER S., Kang H., Bhattacharya M. ve Upal M. (2016) The Primer of Social Media Analytics Journal of Organizational and End User Computing (JOEUC), 28.2: 1-12.
• WEICHSELBRAUN, A., GINDL, S. VE SCHARL, A. (2014) Enriching Semantic Knowledge Bases For Opinion Mining In Big Data Applications Knowledge-Based Systems, 69: 78-85.
• XIANG, Z. Et Al. (2015) What Can Big Data And Text Analytics Tell Us About Hotel Guest Experience And Satisfaction? International Journal Of Hospitality Management, 44: 120-130.
• XIANG, Z., Du, Q., Ma, Y. ve Fan, W. (2017) A Comparative Analysis Of Major Online Review Platforms: Implications For Social Media Analytics In Hospitality And Tourism Tourism Management, 58, 51-65.
• ZAILSKAITE-JAKSTE, L. ve KUVYKAITE, R. (2012) Consumer Engagement İn Social Media By Building The Brand In: Proceedings İn Eiic-1st Electronic International Interdisciplinary Conference.
• ZHENG, X., ZHU, S. ve LIN, Z. (2013) Capturing The Essence Of Word-Of-Mouth For Social Commerce: Assessing The Quality Of Online E-Commerce Reviews By A Semi-Supervised Approach Decision Support Systems, 56: 211-222.