A lesson in remote work and open-source success: exploring emotion classification with farmers at CIMMYT

post by Eliot Jones-Garcia (2019 cohort)

My journey with CIMMYT, the international maize and wheat improvement centre, began shortly before enrolling with the Horizon CDT. In February of 2019, after having recently graduated with an MSc in Rural Development and Innovation from Wageningen University, I found myself in Mexico and in need of a job. I was familiar with the organisation because of its pivotal role in delivering the Green Revolution of the 1960s; an era of significant technological advancement in agriculture enabled by seed breeding innovation achieved by CIMMYT scientists. Since then, they have branched out to engage in several areas of agricultural research, from climate change adaption and mitigation strategies to markets and value chains.

Thus, prior to beginning my PhD research in earnest, I spent 6 months conducting a systematic review of technological change studies for maize systems of the Global South. I worked at the headquarters in Texcoco and gained valuable experience among the various academic disciplines CIMMYT employees use to approach agricultural sustainability. Having forged strong relationships with management staff, they volunteered to support me in my move to Nottingham and transition in research toward ‘digital’ agriculture.

During my first year of research at Horizon, I worked with the CIMMYT staff to conceptualise an internship project. The plan was to head back to Mexico once again in the summer of 2020 to collaborate with scientists there. Unfortunately, however, the unexpected onset of COVID-19 forced me to change plans. At first the work was postponed in the hope the situation would ease but to no avail. I decided to undertake my internship remotely and part-time beginning in January of 2021. In hindsight I was incredibly pleased to have had the initial in-person experience but working at a distance would prove to have its own great lessons.

The goal of my work was to explore different methods of natural language processing, sentiment analysis and emotion classification for analysing interviews with farmers. COVID-19 had not only stunted my travel plans, but all CIMMYT researchers were finding it hard to get to farmers to collect data. These interactions were increasingly taking place remotely, via mobile phones. This removed a significant interpersonal dimension from the research process; without supporting visual context, it became difficult to understand affective elements of conversation. I was given access to a series of interviews with different agricultural stakeholder that had been manually coded according to their use of technology and charged with finding out how these digital tools might aid in analysing audio and textual data.

I approached the task exploring the grounding literature. The first major insight from my internship became how to turn around a thorough and well-argued review for motivating a study in a short time, whilst providing a good understanding for myself and the reader. This yielded a variety of approaches to defining and measuring emotion, selecting audio features for analysis, and modelling tools. I ended up taking a conventional approach, using ready-made R and Python tools and lexicons to analyse text, and a series of widely available labelled datasets to train the model. The second insight from my internship was to engage with different open-source communities and apply available tools to achieve my desired goal.

In combination with working remotely, these activities gave me great confidence to independently deal with tasks, to seek and gain certain skills and to utilise them with support of experts to a high degree of quality. More than anything I feel like this internship taught me to apply my academic abilities to unpack and explore problems in a concise and specific way and to deliver what CIMMYT want in the form of outputs, that is actionable insights that can be applied in a Global South context, and for motivating future research.

In light of this, I produced a structured analysis and report for my CIMMYT supervisors which was then published as an internal discussion paper in December of 2021. Findings from the study indicate that sentiment analysis and emotion classification can indeed support remote interviews and even conventional ethnographic studies. By revealing several biases related to transcription and translation of text, the analyses suggested greater consistency in future study to mitigate any unreliability this may introduce. In terms of affect, there was a clear relationship between different sources of data; dis-adopters of technology, or those who rejected use, were shown to be angrier relative to the rest of the sample, whereas new adopters expressed greater joy and happiness. While this confirmed our expectations there were also unusual insights, for example, female farmers were less fearful in the adoption of technologies. It is expected that in future this research may contribute to better targeted interventions, making technologies available to those who are more likely to make use of them.

Moving forward, I continue to work with my industry partner in smaller projects and look forward to collaborating with them in a professional capacity. This experience has been a great help in my PhD for focusing the direction of my research, highlighting the role of data in shaping how knowledge is created and how that plays into agricultural development. It has helped me to manage tasks and to allocate time wisely, and to produce industry standard work to provide benefit to farmers. The final version of the work is undergoing peer review and I hope to see it published in the near future.

If anyone would like to learn more about this work or would like to contact anyone at CIMMYT, please do not hesitate to contact me at eliot.jones@nottingham.ac.uk.

Many thanks for reading!