Table 1 Summary of the Language Features that Capture Emotional Meaning-Making

Attention

Themes

Motifs

Content words: e.g., bodily sensations (“dizzy,” “headache”)

Function words: e.g., pronouns (“I” vs. “you”)

Co-occurring words/phrases: e.g., ‘entertainment,’ ‘assignments’

Central ideas: e.g., ‘futility’ (“there’s no way”)

Dictionaries^48,52

Topic modeling^42,45

Annotation^43,72

Construal

Dynamism (entities vs. processes)

Distance (proximal vs. distal)

Boundedness (ongoing vs. finite)

Reality status (real vs. hypothetical)

Speaker agency (subject vs. object)

Emotion location (self vs. other)

Word class: e.g., nouns (“anger”) vs. verbs (“to anger”)

Verb tense: e.g., present (“I love”) vs. past (“I loved”)

Verb aspect: e.g., imperfective (“I loved”) vs. perfective (“I had loved”)

Verb mood: e.g., subjunctive (“if I were to see you, I would be happy”)

Verb voice: active (“he scared me”) vs. passive (“I was scared by him”)

Syntactic structure (e.g., “I’m annoyed” vs. “this is annoying”)

Dictionaries^96,106

Natural language processing^122,133

Annotation^116,221

Appraisal

Valence ([un]pleasantness)

Intensity (emotional strength)

Certainty (conviction/confidence)

Positively- (“excited”) and negatively-valenced (“bored”) words

Punctuation (“!”), abbreviations (“LMFO”), emoji (☺), emoticons (:)

Type of emotion label(s) (e.g., “angry” vs. “infuriated”)

Adverbs and modifiers: e.g., hedges (“maybe”) vs. boosters (“very”)

Orthographic features (e.g., “WOAH,” “weeeell”)

Sentence construction (e.g., “I might feel bad about it”)

Dictionaries^32,168

Sentiment algorithms^159,164

Annotation^189,223