Automating the coding of implicit motives (Paper announcement)

The past week has been quite exciting, as we've been in the final stages of preparing a final manuscript on machine learning. I've been rather quiet about this collaboration since the end of 2017, what with the new focus of the project in Zurich, a growing family, and the general pressures of life, not to mention the difficulty of developing a text classifier using neural networks. This particular classification problem is made harder by the fact that motive content is much more diverse in its linguistic manifestation than, for example, sentiment (positive/negative polarity).

This equal-authored paper, however, marks the first phase of what we hope will be a long-running and ultimately successful collaborative research focus on developing a neural network model that can automate the time-consuming nature of assessing implicit motive imagery (a personality measure) in written text. If you're interested in reading about it (we've tried to make the research fields of Implicit Motives, Machine Learning, and Natural Language Processing somewhat accessible), the citation for the online publication is below. And if you follow the URL link, you should be able to read the paper in its entirety (without needing a Springer access account).

• Pang, Joyce S. & Hiram Ring. 2020. Automated coding of implicit motives: A machine-learning approach. Motivation and Emotion, pp. 1–19. doi:10.1007/s11031-020-09832-8. URL https://rdcu.be/b38pm.

If you're interested in checking out how a text classifier for implicit motives might work, you can visit the web app that we built, which uses an underlying CNN model for classification - keep in mind that not all text content has implicit motive imagery, and that this classifier does not yet perform classifications on par with trained human coders. However, it is a start. I may highlight some of the niceties that this particular neural network model can/can't handle in a future post.

In addition to the paper and links above, we provide trained models, data, and some dataset descriptives on the Open Science Framework website, which can be cited and linked to below.

• Pang, Joyce S. & Hiram Ring. 2020. Automating implicit motive coding: Replication data and descriptives. OSF. doi:10.17605/OSF.IO/AURWB. URL https://osf.io/aurwb/.

I am hoping to write up an even more accessible summary of the paper in the coming weeks, but we'll see how it goes. Since the family is currently under COVID 'lockdown' in Singapore with two kids under 2, the only opportunities for doing anything not 'kid-wrangling' related come when the kids are sleeping, which is also when parents have to eat/shower/clean and do housework. This doesn't leave much time for anything else, which is another reason it was exciting to have finished the paper!