Most languages in the world have many different lexemes for emotion. Often, words from different languages convey similar emotional states: for example, the English word love is often translated into Turkish as sevgi and in Hungarian as szerelem. At the same time, it remains unclear whether the concept “love” has the same meaning to speakers of all three languages. If we turn to the ancient Greek language, then four different words at once express different love: agape – soft and sacrificial love, philia – friendly love, something like attraction, storge – kindred love and eros – romantic, passionate love.
In a study published in the journal Science, scientists applied a new method of comparative linguistics to study the meanings of words denoting emotions in various languages of the world.
Scientists found significant variability in the constructed graphs and suggested that the meanings of words denoting emotions can vary between languages even when translations in dictionaries are the same. For example, in the Austronesian languages ”surprise” is closely related to “fear”, while in the Tai-Kadai languages ”surprise” is associated with the concepts of “hope” and “desire”.
“The concept of surprise is especially helpful in understanding research,” says lead author Joshua Conrad Jackson. “Given that one language family has negative associations with a word and another has positive associations, one can imagine how speakers of these different languages might react to people jumping out from behind a sofa or closet, or from a dark room and shouting “surprise!”
In the Austro-Asian languages, regret is closely associated with sadness, but in the Nakh-Dagestan languages - with pity. At the same time, universality was observed: in all the languages considered, sadness was associated with experiences, and joy – with happiness.
A key role in the research was played by the CLICS colexification database, which includes materials from 2474 languages from around the world. Four years ago, the CLICS database only had colexification data in about 300 languages, but new standardization techniques have rapidly increased the volume of data in recent years. Future projects can use this database to learn the semantics of almost any set of concepts.