Abstract
Human natural language is mentioned at a specific point in time while human emotions change over time. While much work has established a strong link between language use and emotional states, few have attempted to model emotional language in time. Here, we introduce the task of affective language forecasting – predicting future change in language based on past changes of language, a task with real-world applications such as treating mental health or forecasting trends in consumer confidence. We establish some of the fundamental autoregressive characteristics of the task (necessary history size, static versus dynamic length, varying time-step resolutions) and then build on popular sequence models for words to instead model sequences of language-based emotion in time. Over a novel Twitter dataset of 1,900 users and weekly + daily scores for 6 emotions and 2 additional linguistic attributes, we find a novel dual-sequence GRU model with decayed hidden states achieves best results (r = .66). We make our anonymized dataset as well as task setup and evaluation code available for others to build on.
Highlights
With the growth of social media, natural language processing has increasingly turned toward problems in the social scientific domains
Knowing how someone’s mood may change week can be a valuable insight into preemptively preparing mental health assistance
We find that applying the dynamic order to a model such as GRU-D, where data can be marked as missing, gives clear benefits when compared against smaller fixed orders
Summary
With the growth of social media, natural language processing has increasingly turned toward problems in the social scientific domains. Language has been used to predict personality disorders, political ideology, and mental health (Preotiuc-Pietro et al, 2016a; Iyyer et al, 2014; Coppersmith et al, 2014). Such predictions are typically made cross-documents or -users rather than cross-time (forecasting the future). Predictions across time can enable another level of applications for NLP in the social sciences. Applications exist in other domains, such as correlating mood and personal buying habits, or predicting the mood of performers (e.g. professional athletes) as insight into how well they will perform We may be able to better predict a substance abuse relapse or suicide attempt, or provide individual insights into the precipitators of such events. Applications exist in other domains, such as correlating mood and personal buying habits, or predicting the mood of performers (e.g. professional athletes) as insight into how well they will perform
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Proceedings of COLING. International Conference on Computational Linguistics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
