Matthew Matero


2020

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Autoregressive Affective Language Forecasting: A Self-Supervised Task
Matthew Matero | H. Andrew Schwartz
Proceedings of the 28th International Conference on Computational Linguistics

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) significantly out-predicting, e.g., a moving averaging based on the past time-steps (r = .49). We make our anonymized dataset as well as task setup and evaluation code available for others to build on.

2019

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Suicide Risk Assessment with Multi-level Dual-Context Language and BERT
Matthew Matero | Akash Idnani | Youngseo Son | Salvatore Giorgi | Huy Vu | Mohammad Zamani | Parth Limbachiya | Sharath Chandra Guntuku | H. Andrew Schwartz
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

Mental health predictive systems typically model language as if from a single context (e.g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e.g. either the message-level or user-level). Here, we bring these pieces together to explore the use of open-vocabulary (BERT embeddings, topics) and theoretical features (emotional expression lexica, personality) for the task of suicide risk assessment on support forums (the CLPsych-2019 Shared Task). We used dual context based approaches (modeling content from suicide forums separate from other content), built over both traditional ML models as well as a novel dual RNN architecture with user-factor adaptation. We find that while affect from the suicide context distinguishes with no-risk from those with “any-risk”, personality factors from the non-suicide contexts provide distinction of the levels of risk: low, medium, and high risk. Within the shared task, our dual-context approach (listed as SBU-HLAB in the official results) achieved state-of-the-art performance predicting suicide risk using a combination of suicide-context and non-suicide posts (Task B), achieving an F1 score of 0.50 over hidden test set labels.