Abstract
Facebook is the most popular platform in the world. Marketers would like to use Facebook user data, which comprises large amounts of information which is useful for marketing. Therefore, analyzing the generation of Facebook users for marketing research is important to successfully capture the target market. In this research, posted data of Thai Facebook users will be analyzed using the combined methods of deep learning and probability of words data. The experiment result yields an accuracy of 82.90% per user and 52.48% per status, which is better than using other models alone such as Multi-Layers Perceptron (MLP), Convolution Neural Networks (CNN), or Long Short-Term Memory (LSTM). The experiment results show that using probability of words in each generation can help to increase the efficiency of the model.
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References
Sarakit, P., Theeramunkong, T., Haruechaiyasak, C., Okumura, M.: Classifying emotion in Thai Youtube comments. In: 2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES), Hua-Hin, pp. 1–5 (2015)
Pholnarat, A.: Distinctive verbs of Thai teenagers’ speech. J. Lang. Cult. 35(Special), 231–235 (2016)
Simaki, V., Mporas, I., Megalooikonomou, V.: Age identification of Twitter users: classification methods and sociolinguistic analysis. In: Gelbukh, A. (ed.) Computational Linguistics and Intelligent Text Processing, CICLing 2016. LNCS, vol. 9624. Springer, Cham (2018)
Bayot, R.K., Gonçalves, T.: Age and gender classification of tweets using convolutional neural networks. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds.) Machine Learning, Optimization, and Big Data, MOD 2017. LNCS, vol. 10710. Springer, Cham (2018)
Rangel, F., Rosso, P., Verhoeven, B., Daelemans, W., Pottast, M., Stein, B.: Overview of the 4th author profiling task at PAN 2016: cross-genre evaluations. In: Balog, K., Cappellato, L., Ferro, N., Macdonald, C. (eds.) Working Notes Papers of the CLEF 2015 Evaluation Labs. CEUR Workshop Proceedings, vol. 1609, pp. 750–784 (2016)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119 (2013)
Kim, Y.: Convolutional Neural Networks for Sentence Classification. CoRR abs/1408.5882 (2014)
Koomsubha, T., Vateekul, P.: A character-level convolutional neural network with dynamic input length for Thai text categorization. In: 2017 9th International Conference on Knowledge and Smart Technology (KST), pp. 101–105. IEEE, Chonburi (2017)
Charoenkwan, P.: ThaiFBDeep: a sentimental analysis using deep learning combined with bag-of-words features on Thai Facebook data. In: 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 565–569. IEEE, Yonago (2018)
Berkup, S.B.: Working with generations X and Y in generation Z period: management of different generations in business life. Mediterr. J. Soc. Sci. 5(19), 218 (2014)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
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Tangtreerat, S., Sinthupinyo, S. (2020). Classification of Generation of Thai Facebook Users Using Deep Learning with Probability of Words. In: Meesad, P., Sodsee, S. (eds) Recent Advances in Information and Communication Technology 2020. IC2IT 2020. Advances in Intelligent Systems and Computing, vol 1149. Springer, Cham. https://doi.org/10.1007/978-3-030-44044-2_6
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