The Patterns of Blends Found in Food and Beverage Names in Instagram

Abstract

The present study discusses the patterns of blends found in the data collected which are food and beverage names found in Instagram and describe the possible new meaning of the blends. The researcher uses Mattiello’s classification of blends as the approach to analyze the data. From the findings, there are fiftythree data that can be considered as blends. The data are classified into three perspectives: the first is morphotactic, the second is morphonological (and graphic), and the third is morphosemantic. The result shows that morphotactically the most productive pattern of the blends is total blend more specifically the blend which the beginning of the first source word is followed by the end of the second source word with 19 data or 34.5 percent. Second, Morphonologically and orthographically, the most productive blend is non-overlapping blends with 31 data or 58.5 percent. Last, morphosemantically, the coordinate blend is more frequent than attributive blends with 30 data or 56.6 percent.