The past couple of decades have seen a substantial increase in linguistic research that highlights the non-arbitrariness of language, as manifested in motivated sound–meaning correspondences. Yet one of the challenges of such studies is that there is a relative paucity of data-driven analyses, especially in the case of languages other than English, such as Hungarian, even though the proportion of at least partially motivated words in Hungarian vocabulary is substantial. We address this gap by investigating the relationship between Hungarian phoneme classes and positive/negative sentiment based on 3,023 word forms retrieved from the Hungarian Sentiment Lexicon. Our results indicate that positive polarity word forms tend to contain more vowels, front vowels, continuants, fricatives, palatals, and sibilants. On the other hand, negative sentiment polarity words tend to have more rounded vowels, plosives, and dorsal consonants. While our analysis provides strong evidence for a set of non-arbitrary form–meaning relationships, effect sizes also reveal that such associations tend to be fairly weak tendencies, and therefore sentiment polarity cannot be derived from the relative frequencies of phoneme classes in a deterministic fashion.

Alleviating pain is good and abandoning hope is bad. We instinctively understand how words like alleviate and abandon affect the polarity of a phrase, inverting or weakening it. When these words are content words, such as verbs, nouns, and adjectives, we refer to them as polarity shifters. Shifters are a frequent occurrence in human language and an important part of successfully modeling negation in sentiment analysis; yet research on negation modeling has focused almost exclusively on a small handful of closed-class negation words, such as not, no, and without. A major reason for this is that shifters are far more lexically diverse than negation words, but no resources exist to help identify them. We seek to remedy this lack of shifter resources by introducing a large lexicon of polarity shifters that covers English verbs, nouns, and adjectives. Creating the lexicon entirely by hand would be prohibitively expensive. Instead, we develop a bootstrapping approach that combines automatic classification with human verification to ensure the high quality of our lexicon while reducing annotation costs by over 70%. Our approach leverages a number of linguistic insights; while some features are based on textual patterns, others use semantic resources or syntactic relatedness. The created lexicon is evaluated both on a polarity shifter gold standard and on a polarity classification task.