Text highlighting is a new method in sensory and consumer science that uses text data. It was developed by the abovementioned authors and co-workers and was first introduced in 2022. It provides an alternative to explicit attitude measurement by rating the degree of agreement/disagreement with statements. It builds on the idea that attitude to a concept/topic can be quantified without the need to rate statements for the degree of agreement/disagreement. In text highlighting, participants are presented with a text focused on the target concept/idea/theme. They are asked to read it and to highlight parts they like/feel positive about and parts they dislike/feel negative about. The highlights are converted to numerical responses, and a sentiment score is calculated. This score reflects attitude to the information described in the text. The present chapter presents the method in full, from its inception to current applications. It describes our current best knowledge about how to conduct text highlighting studies, analyse the data and interpret the findings.