  The extensive use of online social media has highlighted the importance of
privacy in the digital space. As more scientists analyse the data created in
these platforms, privacy concerns have extended to data usage within the
academia. Although text analysis is a well documented topic in academic
literature with a multitude of applications, ensuring privacy of user-generated
content has been overlooked. Most sentiment analysis methods require emotion
labels, which can be obtained through crowdsourcing, where non-expert
individuals contribute to scientific tasks. The text itself has to be exposed
to third parties in order to be labelled. In an effort to reduce the exposure
of online users' information, we propose a privacy preserving text labelling
method for varying applications, based in crowdsourcing. We transform text with
different levels of privacy, and analyse the effectiveness of the
transformation with regards to label correlation and consistency. Our results
suggest that privacy can be implemented in labelling, retaining the
annotational diversity and subjectivity of traditional labelling.
