Brand Analysis in Social Networks Using Deep Learning Techniques

Yıl 2021, Sayı: 27, 386 - 391, 30.11.2021

Fatma Tan , Erkan Yüksel

https://doi.org/10.31590/ejosat.938604

Öz

In recent years, the importance of social media data has increased with the developments in information and communication technologies, and data volume, velocity, variety, veracity, and value have been affected by these developments. Because of the popularity of social networks, the analysis of social media data has also become an important issue for large companies whose brand identity is very crucial. User comments, shares, and explanations in social networks can be used to obtain information about the brand and product. Besides, deep learning techniques, which have become popular recently and provide high accuracy, can be employed for big data analysis in social networks. The number of studies examining the brand image in social networks is quite limited. In this context, we developed a model that performs brand analysis using deep learning techniques in social networks by considering the Starbucks Coffee Company, one of the world's largest coffeehouse chains. We trained our model with Faster Region-based Convolutional Neural Network (Faster R-CNN), Single Shot Multibox Detector (SSD), Mask R-CNN, and You Only Look Once (YOLO) algorithms. We then tested the model on data from Instagram and compared the results. In the light of our results, we have shown that analyzes using deep learning techniques in social networks can significantly affect the image of companies and their brands.

Anahtar Kelimeler

Brand Analysis, Social Networks, Big Data, Deep Learning, Convolutional Neural Networks

Derin Öğrenme Teknikleri Kullanarak Sosyal Ağlarda Marka Analizi

Öz

Son yıllarda bilgi ve iletişim teknolojilerindeki gelişmelerle birlikte sosyal medya verilerinin önemi artmış, veri hacminin yanısıra veri artış hızı, çeşitliliği, doğruluğu ve değeri bu gelişmelerden etkilenmiştir. Sosyal ağların popülaritesi nedeniyle, sosyal medya verilerinin analizi marka kimliği çok önemli olan büyük şirketler için kritik bir konu haline gelmiştir. Marka ve ürün hakkında bilgi edinmek için sosyal ağlardaki kullanıcı yorumlarından, paylaşımlarından ve açıklamalarından faydalanılabilir. Buna ilaveten, son zamanlarda popüler hale gelen ve yüksek doğruluk sağlayan derin öğrenme teknikleri sosyal ağlarda büyük veri analizi için kullanılabilir. Sosyal ağlarda marka imajını inceleyen araştırma sayısı oldukça sınırlıdır. Bu kapsamda, dünyanın en büyük kahve firmalarından biri olan Starbucks örneği ele alınarak sosyal ağlarda derin öğrenme tekniklerini kullanarak marka analizi yapan bir model geliştirdik. Modelimizi Faster Region-based Convolutional Neural Network (Faster R-CNN), Single Shot Multibox Detector (SSD), Mask R-CNN ve You Only Look Once (YOLO) algoritmaları ile eğittik, Instagram sosyal medya verileri üzerinde test ettik ve sonuçları karşılaştırdık. Elde edile sonuçlar ışığında, sosyal ağlarda derin öğrenme teknikleri kullanan analizlerin, şirketlerin ve markaların imajını önemli ölçüde etkileyebileceğini gösterdik.

Anahtar Kelimeler

Marka Analizi, Sosyal Ağlar, Büyük Veri, Derin Öğrenme, Evrişimli Sinir Ağları

Kaynakça

• Boo, S., Busser, J., & Baloglu, S. (2009). A model of customer-based brand equity and its application to multiple destinations. Tourism Management, 30, 219-23.
• Denton, E., Weston, J., Paluri, M., Bourdev, L., & Fergus, R. (2015). User conditional hashtag prediction for images. The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, 1731-1740.
• Diwan, A., Gupta, V., Chadha, C. (2021). Accident detection using mask R-CNN. International Journal for Modern Trends in Science and Technology, 7(01), 69-72.
• Dixon, A. (2020). Efficient clustering for users’ brand sentiment analysis on online social media. University Presentation Showcase Event.
• Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., & Murphy, K. (2017). Speed/accuracy trade-offs for modern convolutional object detectors. IEEE Conference on Computer Vision and Pattern Recognition, 3296-3297.
• Jiang, H., Learned-Miller, E. (2017). Face detection with the faster R-CNN. The 12th IEEE International Conference on Automatic Face and Gesture Recognition, 650-657.
• Kanna, J. S. V., Raj, S. M. E., Meena, M., Meghana, S., Mansoor Roomi, S. (2020). Deep learning based video analytics for person tracking. International Conference on Emerging Trends in Information Technology and Engineering, 1-6.
• Li, D., Wang, R., Xie, C., Liu, L., Zhang, J., Li, R., & Liu, W. (2020). A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network. Sensors, 20(3), 578.
• Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. Leibe, B., Matas, J., & Sebe, N. (Eds). Welling M. Computer Vision-European Conference on Computer Vision, Lecture Notes in Computer Science, vol 9905. Springer, Cham.
• Nguyen, D. T., Alam, F., Ofli, F., & Imran, M. (2017). Automatic image filtering on social networks using deep learning and perceptual hashing during crises. arXiv: 1704.02602.
• Perez, M., Avila, S., Moreira, D., Moraes, D., Testoni, V., Valle, E., & Rocha, A. (2016). Video pornography detection through deep learning techniques and motion information. Neurocomputing, 230, 279-293.
• Porzi, L., Hofinger, M., Ruiz, I., Serrat, J., Bulo, S., & Kontschieder, P. (2020). Learning multi-object tracking and segmentation from automatic annotations. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 6845-6854.
• Redmon, J. & Farhadi, A. (2018). Yolov3: An Incremental Improvement”. arXiv:1804.02767.
• Wang, F., Qi, S., Gao, G., Zhao, S., & Wang, X. (2016). Logo information recognition in large-scale social media data, Multimedia Systems, 22, 63-73.
• Zhao, S., Yao, H., Gao, Y., Ji, R., Xie, W., Jiang, X., & Chua, T. (2016). Predicting personalized emotion perceptions of social images. The 24th ACM International Conference on Multimedia, 1385-1394.