Identifier lexicon has a direct impact on software understandability and reusability and, thus, on the quality of the final software product. Understandability and reusability are two important characteristics of software quality. REST (REpresentational State Transfer) style is becoming a de facto standard adopted by many software organisations. The use of proper lexicon in RESTful APIs might make them easier to understand and reuse by client developers, and thus, would ease their adoption. Linguistic antipatterns represent poor practices in the naming, documentation, and choice of identifiers in the APIs as opposed to linguistic patterns that represent best practices. We present the DOLAR approach (Detection Of Linguistic Antipatterns in REST), which applies syntactic and semantic analyses for the detection of linguistic (anti)patterns in RESTful APIs. We provide detailed definitions of ten (anti)patterns and define and apply their detection algorithms on 15 widely-used RESTful APIs, including Facebook, Twitter, and YouTube. The results show that DOLAR can indeed detect linguistic (anti)patterns with high accuracy and that they do occur in major RESTful APIs.