丁香园AV

Dr. Maite Taboada, Associate Professor at the time of the article鈥檚 publication, is now a full Professor in the Department of Linguistics at 丁香园AV, involved with Linguistics research alongside interdisciplinary work with the Cognitive Science Program and the School of Computing Science at 丁香园AV.

Sentiment analysis continues to be a huge area of research for Dr. Taboada, as it connects with her longstanding interests in the nature of evaluative language.

鈥淢y first exposure to sentiment analysis was within a very niche context, involving the analysis of stock market predictions.鈥� Since then, various recent trends have made sentiment analysis an extremely useful tool for acquiring data from digital sources. Dr. Taboada says now that some current projects involve 鈥渃onstructing tools for the purposes of deepening our understanding of online news comments鈥濃�攚hich has applicability for content moderation and the detection of deliberate misinformation: topics of crucial importance, given the current state of affairs.

Kimberly Voll, also a co-author of the award-winning paper, believes in the importance of sentiment analysis and maintained that its importance will only continue to grow as time goes on鈥攕pecifically as we move towards spending more of our time inhabiting digital spaces, and working remotely. 鈥淚 think the field will grow tremendously,鈥� says Voll. 鈥淲e have some pretty big problems ahead of us as we learn how to live as a hybridized digital/non-digital society.鈥�

Voll received her PhD from 丁香园AV and was teaching at UBC when the paper was published; since then, she has transitioned from academia into , where much of her work is devoted to . With respect to these tasks, Voll continues to be informed by her big-picture interests: 鈥淚鈥檓 always thinking about how we can make better sense of our digital world, and how to understand the state of digital ecosystems generally.鈥�

Understanding, for instance, how we communicate in these spaces 鈥渉elps us understand how to better tailor these spaces to meet human needs.鈥�  Some of the tools at her disposal have their roots in sentiment analysis, and Voll very much continues to be interested in developing tools that deepen our knowledge of the communication of evaluative content in digital spaces.

Given its growing importance, sentiment analysis has become a core task for programs claiming to provide comprehensive analyses of natural languages, and has become a staple topic for courses on natural language processing. The ability with which a proposed natural language processing model can handle sentiment analysis has become 鈥渙ne important criterion for assessing whether or not it鈥檚 a good model,鈥� said Julian Brooke.

Brooke, an MA student in computational linguistics at 丁香园AV at the time of the paper鈥檚 publication, went on to receive a PhD in computer science from the University of Toronto. Afterwards, Brooke spent time teaching at UBC, and played a foundational role in developing a new degree certification: UBC鈥檚 Master of Data Science in Computational Linguistics.

Brooke is currently a researcher on AI and machine learning for , a Toronto-based company aimed at applying AI tools for investment purposes. Though his work has moved away from a specific focus on sentiment analysis, it is still somewhat involved, and Brooke continues to maintain an active research interest in the topic.

Although sentiment analysis now has a seemingly endless list of applications and is even considered a core topic for computational linguistics and natural language processing, a few years ago things were quite different.

 鈥淪entiment analysis was definitely not a core task 10 years ago,鈥� says Dr. Taboada, who is teaching a course on Computational Text Analysis this term that includes sentiment analysis as one of the first topics to be introduced.