Brands and consumers alike have become creators and distributors of digital words, thus generating increasing interest in insights to be gained from text-based content. This work develops an algorithm to identify textual paralanguage, defined as nonverbal parts of speech expressed in online communication. The authors develop and validate a paralanguage classifier (called PARA) using social media data from Twitter, YouTube, and Instagram (N = 1,241,489 posts). Using auditory, tactile, and visual properties of text, PARA detects nonverbal communication cues, aspects of text often neglected by other word-based sentiment lexica. This work is the first to reveal the importance of textual paralanguage as a critical indicator of sentiment valence and intensity. The authors further demonstrate that automatically detected textual paralanguage can predict consumer engagement above and beyond existing text analytics tools. The algorithm is designed for researchers, scholars, and practitioners seeking to optimize marketing communications and offers a methodological advancement to quantify the importance of not only what is said verbally but how it is said nonverbally.
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