Poet Classification Using ANN and DNN

Year 2022, Volume 18, Issue 1, 10 - 20, 30.06.2022

Ekin EKİNCİ Hidayet TAKCI Sultan ALAGÖZ

Abstract

Since statistical analysis of poetry is a challenging task in Natural Language Processing (NLP), making inferences about the poets also becomes a very challenging task. In this study, a dataset of Turkish poems which is obtained for 5 different poets is used to compare classification performance of the Artificial Neural Network (ANN) and Deep Neural Network (DNN) architectures. While Multilayer Perceptron (MLP) is selected for ANN architecture, Convolutional Neural Network (CNN) is selected as DNN architecture. Two main feature representation approaches are used for the experiments- Term Frequency-Inverse Document Frequency (TF-IDF) is used for ANN and word embedding is used for DNN. As a result of the experiments it has been seen that MLP has the highest performance in terms of accuracy, precision, recall and F-score.

Keywords

Poet classification, Classification, Natural Language Processing (NLP), Artificial Neural Network (ANN), Deep Neural Network (DNN)

References

• E. Ekinci, “Using authorship analysis techniques in forensic analysis of electronic mails,” M.S. thesis, Dept. Computer Eng., Gebze Techinal Univ., Kocaeli, Turkey, 2013.
• T. C. Mendenhall, “Characteristic curves of composition,” AAAS, vol. 9, no. 214, pp. 237–246, Mar. 1887, doi: 10.1126/science.ns-9.214S.237.
• F. Mosteller and D. L. Wallace, “Inference in authorship problem,” J. Am. Stat. Assoc., vol. 58, no. 302, pp. 275–309, Jun. 1963, doi: 10.2307/2283270.
• D. Holmes, “Authorship attribution,” Comput. Hum., vol. 28, no. 2, pp. 87–106, Apr. 1994, doi: 10.1007/BF01830689.
• D. Holmes, “The evolution of stylometry in humanities scholarship,” Lit. Ling. Comput., vol. 13, no. 3, pp. 111–117, Sept. 1998, doi: 10.1093/llc/13.3.111.
• A. McEnery and M. Oakes, “Authorship studies/textual statistics,” in Handbook of Natural Language Processing, R. Dale, H. Moisl and H. Somers, Eds., Dallas, USA: Marcel Dekker Inc., 2000, pp. 234–248.
• A. Rico-Sulayes, “Statistical authorship attribution of Mexican drug traficking online forum posts,” Int. J. Speech, Lang. Law, vol. 18, no. 1, pp. 53–74, Sept. 2011, doi: 10.1558/ijsll.v18i1.53.
• P. Hajek and R. Henriques, “Mining corporate annual reports for intelligent detection of financial statement fraud--A comparative study of machine learning methods,” Knowl.-Based Syst., vol. 128, pp. 139–152, Jul. 2017, doi: 10.1016/j.knosys.2017.05.001.
• R. Jindal, R. Malhotra and A. Jain, “Techniques for text classification: literature review and current trends,” Webology, vol. 12, no. 2, 2015.
• R. Zheng, J. Li, H. Chen and Z. Huang, “A framework for authorship identification of online messages: writing-style features and classification techniques,” JASIST, vol. 57, no. 3, pp. 378–393, Dec. 2005, doi: 10.1002/asi.20316.
• S. Ouamour and H. Sayoud, “Authorship attribution of ancient texts written by ten Arabic travelers using a smo-svm classifier,” in Proc. 2012 Int. Conf. on Communications and Information Technology (ICCIT), Jun. 2012, pp. 44–47, doi: 10.1109/ICCITechnol.2012.6285841.
• P. Varela, M. Albonico, E. Justino and J. Assis, “Authorship attribution in Latin languages using stylometry,” IEEE Lat. Am. Trans., vol. 18, no. 4, pp. 729–735, Apr. 2020, doi: 10.1109/TLA.2020.9082216.
• A. Pandian, V. V. Ramalingam and R. P. V. Preet, “Authorship identification for Tamil classical poem (Mukkoodar Pallu) using C4.5 algorithm,” Indian J. Sci., vol. 9, no. 47, pp. 1–5, Dec. 2016, doi: 10.17485/ijst/2016/v9i47/107944.
• A. S. Altheneyan and M. C. Menai, “Naïve Bayes classifiers for authorship attribution of Arabic texts,” J. King Saud Univ., Comp. & Info. Sci., vol. 26, no. 4, pp. 473–484, Dec. 2014, doi: 10.1016/j.jksuci.2014.06.006.
• D. M. Anisuzzaman and A. Salam, “Authorship attribution for Bengali language using the fusion of n-gram and naive bayes algorithms,” IJITCS, vol. 10, pp. 11–21, Oct. 2018, doi: 10.5815/ijitcs.2018.10.02.
• H. Zamani, H. N. Esfahani, P. Babaie, S. Abnar, M. Dehghani and A. Shakery, “Authorship identification using dynamic selection of features from probabilistic feature set,” in Information Access Evaluation. Multilinguality, Multimodality, and Interaction, vol. 8685, E. Kanoulaset al., Eds., Switzerland: Springer, Cham, 2014, pp. 128–140.
• R. L. Priya and G. Manimannan, “Authorship attribution of Tamil articles using artificial neural network,” IJSIMR, vol. 3, no. 6, pp. 22–28, Jun. 2015.
• X. Yang, G. Xu, Q. Li, Y. Guo and M. Zhang, “Authorship attribution of source code by using back propagation neural network based on particle swarm optimization,” PLoS ONE, vol. 12, no. 11, pp. 1–18, Nov. 2017, doi: 10.1371/journal.pone.0187204.
• M. Al-Sarem, F. Saeed, A. Alsaeedi, W. Boulila and T. Al-Hadhrami, “Ensemble methods for instance-based Arabic language authorship attribution,” IEEE Access, vol. 8, pp. 17331–17345, Jan. 2020, doi: 10.1109/ACCESS.2020.2964952.
• C. Suman, A. Raj, S. Saha and P. Bhattacharyya, “Source code authorship attribution using stacked classifier,” presented at the 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020), Hyderabad, India, Dec. 16-20, 2020.
• C. Zhao, W. Song, X. Liu, L. Liu and X. Zhao, “Research on authorship attribution of article fragments via RNNs,” in Proc. 2018 IEEE 9th Int. Conf. on Software Engineering and Service Science (ICSESS), Nov. 2018, pp. 156–159, doi: 10.1109/ICSESS.2018.8663814.
• B. Alsulami, E. Dauber, R. Harang, S. Mancoridis and R. Greenstadt, “Source code authorship attribution using long short-term memory based networks,” in Computer Security – ESORICS 2017, vol. 10492, S. Foley, D. Gollmann and E. Snekkenes, Eds., Switzerland: Springer, Cham, 2017
• P. Shrestha, S. Sierra, F. A. Gonzalez, M. Montes, P. Rosso and T. Solorio, “Research on authorship attribution of article fragments via RNNs,” in Proc. 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain, 2017, pp. 669–674.
• A. Khatun, A. Rahman, S. Islam and M. E. Jannat, “Authorship attribution in Bangla literature using character-level CNN,” in Proc. 2019 22nd Int. Conf. on Computer and Information Technology (ICCIT), 2019, pp. 1–5, doi: 10.1109/ICCIT48885.2019.9038560.
• V. Kalgutkar, R. Kaur, H. Gonzalez, N. Stakhanova and A. Matyukhina, “Code authorship attribution: methods and challenges,” ACM Comput. Surv., vol. 52, no.1, pp. 1–36, Feb. 2019, doi: 10.1145/3292577.
• R. Mateless, O. Tsur and R. Moskovitch, “Pkg2Vec: Hierarchical package embedding for code authorship attribution,” Future Gener. Comput. Syst., vol. 116, pp. 49–60, Mar. 2021, doi: 10.1016/j.future.2020.10.020.
• M. R. Schmid, F. Iqbal and B. C. M. Fung, “E-mail authorship attribution using customized associative classification,” Digit. Investig., vol. 14, no. 1, pp. S116–S126, Aug. 2015, doi: 10.1016/j.diin.2015.05.012.
• Y. Fang, Y. Yang and C. Huang, “EmailDetective: an email authorship identification and verification model,” Comput. J., vol. 63, no. 11, pp. 1775–1787, Jul. 2020, doi: 10.1093/comjnl/bxaa059.
• M. H. Altakrori, F. Iqbal, B. C. M. Fung, S. H. H. Ding and A. Tubaishat, “Arabic authorship attribution: an extensive study on twitter posts,” ACM Trans. Asian Low-Resour. Lang. Inf. Process., vol. 18, no. 1, pp. 1–51, Nov. 2018, doi: 10.1145/3236391.
• C. Suman, A. Raj, S. Saha and P. Bhattacharyya, “Authorship attribution of microtext using capsule networks,” IEEE Trans. Comput. Soc. Syst., Apr. 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9393500.
• T. Cavalcante, A. Rocha and A. Carvalho, “Large-scale micro-blog authorship attribution: beyond simple feature engineering,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, vol. 8827, E. Bayro-Corrochano and E. Hancock, Eds., Switzerland: Springer, Cham, 2014, pp. 399–407.
• P. Canbay, E. A. Sezer and H. Sever, “Binary background model with geometric mean for author-independent authorship verification,” J. Inf. Sci., pp. 1–17, May. 2021, doi: 10.1177/01655515211007710.
• G. R. Casimiro and L. A. Digiampietri, “Authorship attribution using data from Reddit forum,” in Proc. SBSI'20: XVI Brazilian Symposium on Information Systems, 2020, pp. 1–8, doi: 10.1145/3411564.3411616.
• M. Kestemont, “Stylometric authorship attribution for the middle Dutch mystical tradition from Groenendaal,” Dutch Crossing, vol. 42, no. 1, pp. 1–51, Nov. 2018, doi: 10.1145/3236391.
• T. Boran, M. Martinaj and M. S. Hossain, “Authorship identification on limited samplings,” Comput. Secur., vol. 97, pp. 101943, Oct. 2020, doi: 10.1016/j.cose.2020.101943.
• J. F. Hoorn, S. L. Frank, W. Kowalczyk and F. van der Ham, “Neural network identification of poets using letter sequences,” Lit. Linguistics Comput., vol. 14, no. 3, pp. 311–338, Sep. 1999, doi: 10.1093/llc/14.3.311.
• A. S. Shahmiri, R. Dezhkam and S. Shirey, “Poet identification for Shahnameh of Ferdowsi using artificial neural networks,” The CSI Journal on Computer Science and Engineering, vol. 4, no. 3(a), pp. 17–26, 2006.
• I. A. Mohammad, “Naïve Bayes for Classical Arabic Poetry Classification,” Al-Nahrain Journal of Science, vol. 12, no. 4, pp. 217–225, Dec. 2009.
• E. F. Can, F. Can, P. Duygulu and M. Kalpakli, “Automatic categorization of Ottoman literary texts by poet and time period,” in Computer and Information Sciences II, E. Gelenbe, R. Lent and G. Sakellari, Eds., UK: Springer, London, 2012, pp. 52–57.
• G. Rakshit, A. Ghosh, P. Bhattacharyya and G. Haffari, “Automated analysis of Bangla poetry for classification and poet identification,” in Proc12th Intl. Conference on Natural Language Processing, Trivandrum, India, 2015, pp. 247–253.
• A. Ahmed, R. Mohamed and B. Mostafa, “Authorship attribution in Arabic poetry using NB, SVM, SMO,” in Proc. 2016 11th International Conf. on Intelligent Systems: Theories and Applications (SITA), 2016, pp. 1–5, doi: 10.1109/SITA.2016.7772287.
• A. Ahmed, R. Mohamed and B. Mostafa, “Machine learning for authorship attribution in Arabic poetry,” IJFCC, vol. 6, no. 2, pp. 42–46, June 2017, doi: 10.18178/ijfcc.2017.6.2.486.
• S. Waijanya and N. Promrit, “The poet identification using convolutional neural networks,” in Intelligent Systems and Computing, vol. 566, P. Meesad, S. Sodsee and H. Unger, Eds., Switzerland: Springer, Cham, 2017, pp. 179–187.
• D. Ö. Şahin, O. E. Kural, E. Kılıç and A. Karabina, “A text classification application: poet detection from poetry,” in Proc. International Conf. on Engineering Technologies (ICENTE’17), Konya, Turkey, 2017, pp. 228–230.
• N. Tariq, I. Ezaj, M. K. Malik, Z. Nawaz and F. Bukhari, “Identification of Urdu ghazal poets using SVM,” Mehran University Research Journal of Engineering & Technology, vol. 38, no. 4 pp. 935–944, Oct. 2019, doi: 10.22581/muet1982.1904.07.
• A. Ahmed, R. Mohamed and B. Mostafa, “Machine learning for authorship attribution in Arabic poetry,” DHS, pp. 1–10, May 2020, doi: 10.1093/llc/fqz096.
• H. Christian, M. P. Agus and D. Suhartono, “Single document automatic text summarization using term frequency-inverse document frequency (TF-IDF),” ComTech: Computer, Mathematics and Engineering Applications, vol. 7, no. 4, pp. 285–294, Dec. 2016.
• W. Singleton and M. El-Sharkawy, “Increasing cnn representational power using absolute cosine value regularization,” in 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), Springfield, MA, USA, 2020, pp. 391-394.