Affect Experience in Everyday Language Logged with Smartphones

Analyzing language offers a unique window into the inner workings of the human mind. Particularly, the methodological advancements in text processing in recent years and the ubiquity of textual digital trace data have generated opportunities to investigate psychological constructs, such as affective states, through language in the form of text. In this manner, prior studies have predicted affective states from text data, for example social media posts. Most prior studies relied on human annotations or on establishing sentiment through lexica (e.g., LIWC, VADER) to infer users’ affect from social media language samples (e.g., Facebook status updates). However, affective word usage and human judges’ rating conceptually differ from one’s subjective affect experience. Here, phone-level data collection methods offer a promising opportunity to passively log textual data across communication channels (public and private contexts) through the smartphone’s keyboard and couple the data with in-situ self-reports on one’s affect through experience sampling. In this work, we want to investigate (in-sample) associations of in-situ self-reported affective states with language features logged with smartphones in everyday life and if these features allow for the (out-of-sample) prediction of between-person differences and within-person fluctuations in affect experience. Further, we want to investigate which language features are most predictive of subjective affect experience. Finally, we want to analyze what the optimal time window is for text analysis (and corresponding amount of text data) around the timestamp of the affective state in question and how affect experience is revealed in different contexts (e.g., public posting vs. private messaging). unknown other