There is little research into designing artificial motivational agents. The end-goal of our studies is therefore to create a dialogue system that would motivate users to do their everyday tasks using natural language. In this paper, we present a method of distinguishing texts containing motivational advice from regular texts to sort out noise in training data for our dialogue system. We implemented a novel method of chaining two shallow networks together by utilizing the output results of the first network to determine the input for the second one. We achieved F-score of 0.94 and 0.97 with our proposed method. The contributions of this paper are threefold: first, we successfully identified 14 hand-crafted features that make a text motivational/advisory. Secondly, we were able to create a classifying algorithm that distinguishes motivational/advisory texts from regular ones. Finally, our proposed method can be applied to other text classification tasks.

1. [1] R. M. Badubi, “Theories of motivation and their application in organizations: A risk analysis,” Int. J. of Innovation and Economic Development, Vol.3, No.3, pp. 43-50, 2017.
2. [2] B. Gerhart and M. Fang, “Pay, intrinsic motivation, extrinsic motivation, performance, and creativity in the workplace: Revisiting long-held beliefs,” Annual Review of Organizational Psychology and Organizational Behavior, Vol.2, pp. 489-521, 2015.
3. [3] S. Kultalahti and R. L. Viitala, “Sufficient challenges and a weekend ahead–Generation Y describing motivation at work,” J. of Organizational Change Management, Vol.27, No.4, pp. 569-582, 2014.
4. [4] A. Elmelid, A. Stickley, F. Lindblad, M. Schwab-Stone, C. C. Henrich, and V. Ruchkin, “Depressive symptoms, anxiety and academic motivation in youth: Do schools and families make a difference?,” J. of Adolescence, Vol.45, pp. 174-182, 2015.
5. [5] D. Litalien, F. Guay, and A. J. S. Morin, “Motivation for PhD studies: Scale development and validation,” Learning and Individual Differences, Vol.41, pp. 1-13, 2015.
6. [6] L. M. Fussner, K. J. Mancini, and A. M. Luebbe, “Depression and approach motivation: Differential relations to monetary, social, and food reward,” J. of Psychopathology and Behavioral Assessment, Vol.40, No.1, pp. 117-129, 2018.
7. [7] R. Hershenberg, “Activating happiness: A jump-start guide to overcoming low motivation, depression, or just feeling stuck,” New Harbinger Publications, 2017.
8. [8] HealthDirect, “Motivation: How to get started and staying motivated,” 2018, https://www.healthdirect.gov.au/motivation-how-to-get-started-and-staying-motivated [accessed October 23, 2018]
9. [9] R. Nussbaum, “18 ways to motivate yourself that actually work,” 2017, Greatist, https://greatist.com/grow/motivation-tips-that-work [accessed October 23, 2018]
10. [10] Reddit, “Get Disciplined,” 2018, https://www.reddit.com/r/getdisciplined/ [accessed October 2, 2018]
11. [11] Reddit, “Today I Learned,” 2018, https://www.reddit.com/r/todayilearned/ [accessed May 28, 2018]
12. [12] Reddit, “API Documentation,” 2018, https://www.reddit.com/dev/api/ [accessed November 27, 2018]
13. [13] E. Cambria, S. Poria, D. Hazarika, and K. Kwok, “SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings,” Proc. of 32nd Association for the Advancement of Artificial Intelligence (AAAI) Conf. on Artificial Intelligence, pp. 1795-1802, 2018.
14. [14] S. Deshpande, G. K. Palshikar, and G. Athiappan, “An unsupervised approach to sentence classification,” Proc. of Int. Conf. on Management of Data COMAD, pp. 88-99, 2010.
15. [15] Princeton University, “About WordNet,” 2010, https://wordnet.princeton.edu [accessed May 15, 2018]
16. [16] G. Zhu and C. A. Iglesias, “Computing semantic similarity of concepts in knowledge graphs,” IEEE Trans. on Knowledge and Data Engineering, Vol.29, No.1, pp. 72-85, 2017.
17. [17] S. Bird, E. Klein, and E. Loper, “Natural Language Toolkit,” 2018, https://www.nltk.org/ [accessed May 15, 2018]
18. [18] P. Swieczkowska, R. Rzepka, and K. Araki, “Analyzing motivating texts for modelling human-like motivation techniques in emotionally intelligent dialogue systems,” Proc. of Biologically Inspired Cognitive Architectures Meeting, pp. 355-360, 2018.
19. [19] D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” Proc. of the 3rd Int. Conf. for Learning Representations, 2014.
20. [20] X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” Proc. of the 13th Int. Conf. on Artificial Intelligence and Statistics, pp. 249-256, 2010.
21. [21] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” Proc. of the 26th Int. Conf. on Neural Information Processing Systems, pp. 3111-3119, 2013.
22. [22] Wikimedia Foundation, “Wikipedia dump,” 2018, https://dumps.wikimedia.org/backup-index.html [accessed February 8, 2018]
23. [23] S. Arora, Y. Liang, and T. Ma, “A simple but tough-to-beat baseline for sentence embeddings,” 2017, https://openreview.net/pdf?id=SyK00v5xx [accessed April, 15 2017]
24. [24] Nealrs, “Contractions.py,” 2014, https://gist.github.com/nealrs/96342d8231b75cf4bb82 [accessed November 27, 2018]
25. [25] Mammothb, “Symspellpy,” 2018, https://github.com/mammothb/symspellpy [accessed November 27, 2018]
26. [26] Z. Callejas and D. Griol, “An affective utility model of user motivation for counselling dialogue systems,” Proc. of Int. Workshop on Future and Emerging Trends in Language Technology, pp. 86-97, 2016.
27. [27] B. Weiner, “An attribution theory of motivation and emotion,” Psychological Review, Vol.92, No.4, pp. 548-573, 1985.
28. [28] M. Tielman, M. Neerincx, and W.-P. Brinkman, “Generating situation-based motivational feedback in a PTSD E-health system,” Proc. of Int. Conf. on Intelligent Virtual Agents, pp. 437-440, 2017.
29. [29] H. A. He, S. Greenberg, and E. M. Huang, “One size does not fit all: Applying the transtheoretical model to energy feedback technology design,” Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, pp. 927-936, 2010.
30. [30] E. C. Grigore, A. Pereira, and B. Scassellati, “Modeling motivational states in adaptive robot companions,” Proc. of Association for the Advancement of Artificial Intelligence (AAAI) Fall Symp., pp. 77-80, 2015.
31. [31] B. J. Fogg, “A behavior model for persuasive design,” Proc. of the 4th Int. Conf. on Persuasive Technology, Article No.40, 2009.
32. [32] H. Oinas-Kukkonen, “A foundation for the study of behavior change support systems,” Personal and Ubiquitous Computing, Vol.17, No.6, pp. 1223-1235, 2013.
33. [33] L. Süssenbach, N. Riether, S. Schneider, I. Berger, F. Kummert, I. Lütkebohle, and K. Pitsch, “A robot as fitness companion: Towards an interactive action-based motivation model,” Proc. of the 23rd IEEE Int. Symp. on Robot and Human Interactive Communication, pp. 286-293, 2014.
34. [34] S. Schneider and F. Kummert, “Motivational effects of acknowledging feedback from a socially assistive robot,” Proc. of Int. Conf. on Social Robotics, pp. 870-879, 2016.
35. [35] M. Kaptein, B. de Ruyter, P. Markopoulos, and E. Aarts, “Adaptive persuasive systems: A study of tailored persuasive text messages to reduce snacking,” ACM Trans. on Interactive Intelligent Systems (TiiS), Vol.2, No.2, Article No.10, 2012.
36. [36] M. Kaptein, P. Markopoulos, B. de Ruyter, and E. Aarts, “Can you be persuaded? Individual differences in susceptibility to persuasion,” Proc. of IFIP Conf. on Human-Computer Interaction, pp. 115-118, 2009.
37. [37] S.-B. Cho and J. H. Kim, “Combining multiple neural networks by fuzzy integral for robust classification,” IEEE Trans. on Systems, Man, and Cybernetics, Vol.25, No.2, pp. 380-384, 1995.
38. [38] A. K. Jerebko, J. D. Malley, M. Franaszek, and R. M. Summers, “Multiple neural network classification scheme for detection of colonic polyps in CT colonography data sets,” Academic Radiology, Vol.10, No.2, pp. 154-160, 2003.
39. [39] K. Tsuda, H. Shin, and B. Schölkopf, “Fast protein classification with multiple networks,” Bioinformatics, Vol.21, No.Suppl_2, pp. ii59-ii65, 2005.
40. [40] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Proc. of Advances in Neural Information Processing Systems, pp. 2672-2680, 2014.
41. [41] G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” Proc. of the 32nd Int. Conf. on Machine Learning (ICML), Deep Learning Workshop, Vol.2, 2015.
42. [42] G. Gkioxari, A. Toshev, and N. Jaitly, “Chained predictions using convolutional neural networks,” Proc. of European Conf. on Computer Vision, pp. 728-743, 2016.