References
- Aggarwal, C. C., & Zhai, C. (2012). Mining text data. Springer science & business media.
- Aggarwal, C. C. (2018). Machine learning for text. Springer: Cham, Switzerland.
- Baumgartner, F. R., Jones, B. D., & Mortensen, P. B. (2017). Punctuated equilibrium theory: explaining stability and change in public policymaking. In Theories of the policy process (4th ed., pp. 55–101). Routledge.
- Baumgartner, F. R., Breunig, C., & Grossman, E. (2019). Comparative policy agendas: Theory, tools, data. Oxford University Press.
- Benoit, K., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Mu¨ller, S., & Matsuo, A. (2018). quanteda: an R package for the quantitative analysis of textual data. Journal of Open Source Software, 3(30), 774. doi:10.21105/joss.00774
- Bischl, B., Lang, M., Kotthoff, L., Schiffner, J., Richter, J., Studerus, E., Jones, Z. M. (2016). mlr: machine learning in r. The Journal of Machine Learning Research, 17(1), 5938–5942.
- Borghetto, E., & Russo, F. (2018). From agenda setters to agenda takers? The determinants of party issue attention in times of crisis. Party Politics, 24(1), 65–77. doi:10.1177/1354068817740757
- Borghetto, E., & Chaques-Bonafont, L. (2019). Parliamentary questions. in comparative policy. In agendas (pp. 282–299). New York: Oxford University Press Oxford. doi: 10.1093/oso/9780198835332.001.0001
- Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., & Dhariwal, P., others (2020). Language models are few-shot learners.
- Burdisso, S. G., Errecalde, M., & Montes-y G´omez, M. (2019). A text classification framework for simple and effective early depression detection over social media streams. Expert Systems with Applications, 133, 182–197. doi:10.1016/j.eswa.2019.05.023
- Burscher, B., Vliegenthart, R., & De Vreese, C. H. (2015). Using supervised machine learning to code policy issues: can classifiers generalize across contexts? The ANNALS of the American Academy of Political and Social Science, 659(1), 122–131. doi:10.1177/0002716215569441
- Collingwood, L., & Wilkerson, J. (2012). Tradeoffs in accuracy and efficiency in supervised learning methods. Journal of Information Technology & Politics, 9(3), 298–318. doi:10.1080/19331681.2012.669191
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. doi:10.1007/BF00994018
- Courtney, M., Breen, M., McMenamin, I., & McNulty, G. (2020). Automatic translation, context, and supervised learning in comparative politics. Journal of Information Technology& Politics, 17(3), 208–217. doi:10.1080/19331681.2020.1731245
- Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., & Singer, Y. (2006). Online passive- aggressive algorithms. Journal of Machine Learning Research, 7(Mar), 551–585.
- D’Andrea, E., Ducange, P., Lazzerini, B., & Marcelloni, F. (2015). Real-time detection of traffic from twitter stream analysis. IEEE Transactions on Intelligent Transportation Systems, 16(4), 2269–2283. doi:10.1109/TITS.2015.2404431
- D’Andrea, E., Ducange, P., Bechini, A., Renda, A., & Marcelloni, F. (2019). Monitoring the public opinion about the vaccination topic from tweets analysis. Expert Systems with Applications, 116, 209–226. doi:10.1016/j.eswa.2018.09.009
- Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019, June). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proc. of the 2019 conf. of the North American chapter of the association for computational linguistics: Human lan- guage technologies (4171–4186,Vol. 1 (long and short papers). Minneapolis, Minnesota: Association for Computational Linguistics.
- Garc´ıa, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: A case study on the cec’2005 special session on real parameter optimization. Journal of Heuristics, 15(6), 617–644. doi:10.1007/s10732-008-9080-4
- Gilardi, F., & Wu¨est, B. (2020). Using text-as-data methods in comparative policy analysis. In Handbook of research methods and applications in comparative policy analysis. Edward Elgar Publishing.
- Grave, E., Bojanowski, P., Gupta, P., Joulin, A., & Mikolov, T. (2018). Learning word vectors for 157 languages. arXiv preprint, arXiv, 1802.06893.
- Grimmer, J. (2010). A Bayesian hierarchical topic model for political texts: measuring ex- pressed agendas in senate press releases. Political Analysis, 18(1), 1–35. doi:10.1093/pan/mpp034
- Grimmer, J., & King, G. (2011). General purpose computer-assisted clustering and concep- tualization. Proceedings of the National Academy of Sciences, 108(7), 2643–2650. doi:10.1073/pnas.1018067108
- Grimmer, J., & Stewart, B. M. (2013). Text as data: the promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297. doi:10.1093/pan/mps028
- Hansen, D. H., Navarretta, C., Offersgaard, L., & Wedekind, J. (2019). Towards the automatic classification of speech subjects in the danish parliament corpus. Proceedings of the Digital Humanities in the Nordic Countries 4th Conference, Copenhagen, Denmark, March 5-8, 2019. CEUR Workshop Proceedings 2364, 166–174.
- Haykin, S. (1998). Neural networks: A comprehensive foundation (2nd ed.). Prentice Hall PTR. Upper Saddle River, USA.
- Hillard, D., Purpura, S., & Wilkerson, J. (2008). Computer-assisted topic classification for mixed-methods social science research. Journal of Information Technology & Politics, 4(4), 31–46. doi:10.1080/19331680801975367
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, (8). doi:10.1162/neco.1997.9.8.1735
- Hughes, M., Li, I., Kotoulas, S., & Suzumura, T. (2017). Medical text classification using convolutional neural networks. Studies in Health Technology and Informatics, 235(235), 246–250.
- Hussain, F. (2017). Internet of everything. In Internet of things: building blocks and business. In models (pp. 1–11). Springer International Publishing: Germany.
- Ikonomakis, M., Kotsiantis, S., & Tampakas, V. (2005). Text classification using machine learning techniques. WSEAS Transactions on Computers, 4(8), 966–974.
- Iyyer, M., Enns, P., Boyd-Graber, J., & Resnik, P. (2014). Political ideology detection using recursive neural networks. In Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 1: Long papers), Baltimore, Maryland, USA (pp. 1113–1122).
- Jurka, T. P., Collingwood, L., Boydstun, A. E., Grossman, E., & van Atteveldt, W. (2013). Rtexttools: A supervised learning package for text classification. R Journal, 5(1), 6–12. doi:10.32614/RJ-2013-001
- Khan, A., Baharudin, B., Lee, L. H., & Khan, K. (2010). A review of machine learning algorithms for text-documents classification. Journal of Advances in Information Technology, 1(1), 4–20.
- Kim, Y. (2014). Convolutional neural networks for sentence classification. in proceedings of the 2014 conference on empirical methods in natural language processing, Doha, Qatar (pp. 1746–1751).
- Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text classification algorithms: A survey. Information, 10(4), 150. doi:10.3390/info10040150
- Kuhn, M. (2008). Building predictive models in r using the caret package. Journal of Statistical Software, 28(5), 1–26. doi:10.18637/jss.v028.i05
- Kusner, M., Sun, Y., Kolkin, N., & Weinberger, K. (2015). From word embeddings to document distances. in international conference on machine learning, Lille, France (pp. 957–966).
- Lin, Y., Meng, Y., Sun, X., Han, Q., Kuang, K., Li, J., & Wu, F. (2021, August). Bert- GCN: transductive text classification by combining GNN and BERT. In Findings of the association for computational linguistics: Acl-ijcnlp 2021 (pp. 1456–1462). Online.
- Liu, Y., Liu, Z., Chua, T.-S., & Sun, M. (2015). Topical word embeddings. In Twenty-ninth aaai conference on artificial intelligence. Austin Texas, USA.
- Loftis, M. W., & Mortensen, P. B. (2020). Collaborating with the machines: A hybrid methodfor classifying policy documents. Policy Studies Journal, 48(1), 184–206. doi:10.1111/psj.12245
- Mettler, S. (2016). The policyscape and the challenges of contemporary politics to policy maintenance. Perspectives on Politics, 14(2), 369–390. doi:10.1017/S1537592716000074
- Miron´czuk, M. M., & Protasiewicz, J. (2018). A recent overview of the state-of-the-art elements of text classification. Expert Systems with Applications, 106, 36–54.
- Navarretta, C., & Hansen, D. H. (2020). Identifying parties in manifestos and parliament speeches. in proceedings of the second parlaclarin workshop, Marseille, France (pp. 51–57).
- Neumann, A., Laranjeiro, N., & Bernardino, J. (2018). An analysis of public rest web service apis. IEEE Transactions on Services Computing, 14(4), 957 - 970.
- Novotny´, V., Ayetiran, E. F., Sˇtef´anik, M., & Sojka, P. (2020). Text classification with wordembedding regularization and soft similarity measure. arXiv.
- Onan, A., Koruko˘glu, S., & Bulut, H. (2016). Ensemble of keyword extraction methods and classifiers in text classification. Expert Systems with Applications, 57, 232–247. doi:10.1016/j.eswa.2016.03.045
- Onan, A. (2018). An ensemble scheme based on language function analysis and feature engineering for text genre classification. Journal of Information Science, 44(1), 28–47. doi:10.1177/0165551516677911
- Onan, A. (2019). Two-stage topic extraction model for bibliometric data analysis based on word embeddings and clustering. IEEE Access, 7, 145614–145633. doi:10.1109/ACCESS.2019.2945911
- Onan, A. (2020a). Mining opinions from instructor evaluation reviews: A deep learning approach. Computer Applications in Engineering Education, 28(1), 117–138. doi:10.1002/cae.22179
- Onan, A. (2020b). Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurrency and Computation: Practice and Experience, 33(23), e5909. Wiley Online Library.
- Onan, A., & To¸co˘glu, M. A. (2021). A term weighted neural language model and stacked bidirectional lstm based framework for sarcasm identification. IEEE Access, 9, 7701–7722. doi:10.1109/ACCESS.2021.3049734
- Pontes, F. J., Amorim, G., Balestrassi, P. P., Paiva, A., & Ferreira, J. R. (2016). Design of experiments and focused grid search for neural network parameter optimization. Neuro- Computing, 186, 22–34.
- Purpura, S., & Hillard, D. (2006). Automated classification of congressional legislation. In Proceedings of the 2006 international conference on digital government research, San Diego California USA (pp. 219–225).
- Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010). How to analyze political attention with minimal assumptions and costs. American Journal of Political Science, 54(1), 209–228. doi:10.1111/j.1540-5907.2009.00427.x
- Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of naive bayes text classifiers. In Proceedings of the 20th international conference on machine learning (icml-03), Washington, DC, USA (pp. 616–623).
- Rosenthal, S., Farra, N., & Nakov, P. (2017). Semeval-2017 task 4: sentiment analysis in twitter. In Proceedings of the 11th international workshop on semantic evaluation (semeval- 2017), Vancouver, Canada (pp. 502–518).
- Russo, F., & Cavalieri, A. (2016). The policy content of the Italian question time. a new dataset to study party competition. Rivista Italiana di Politiche Pubbliche, 11(2), 197–222.
- Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009). The graph neural network model. IEEE Transactions on Neural Networks, 20(1), 61–80. doi:10.1109/TNN.2008.2005605
- Slapin, J. B., & Proksch, S.-O. (2014). Words as data: content analysis in legislative studies. In The Oxford handbook of legislative. In studies (pp. 126–144). Oxford University Press Oxford: UK.
- Syarif, I., Prugel-Bennett, A., & Wills, G. (2016). Svm parameter optimization using grid search and genetic algorithm to improve classification performance. Telkomnika, 14(4), 1502. doi:10.12928/telkomnika.v14i4.3956
- Tharwat, A. (2018). Classification assessment methods. Applied Computing and Informatics.
- van Atteveldt, W., Welbers, K., & van der Velden, M. (2019). Studying political decision making with automatic text analysis. Oxford Research Encyclopedia of Politics, 1–11.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. (2017). Attention is all you need. In Proc. Of the 31st int’l conf. On neural information processing systems (pp. 6000–6010). Curran Associates Inc.
- Wiedemann, G. (2019). Proportional classification revisited: automatic content analysis of political manifestos using active learning. Social Science Computer Review, 37(2), 135–159. doi:10.1177/0894439318758389
- Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Rush, A. M. (2020). Huggingface’s transformers: State-of-the-art natural language processing.
- Yoo, S., Song, J., & Jeong, O. (2018). Social media contents based sentiment analysis and prediction system. Expert Systems with Applications, 105, 102–111. doi:10.1016/j.eswa.2018.03.055
- Zhang, Y., Jin, R., & Zhou, Z.-H. (2010). Understanding bag-of-words model: A statistical framework. International Journal of Machine Learning and Cybernetics, 1(1–4), 43–52. doi:10.1007/s13042-010-0001-0