Giving computers the ability to understand the user's mood and feelings is the aim for affective computing field. This ability would enhance the interaction between the user and his/her computer to create advanced systems for education, commerce, security and mental disorder diagnosis, among other functions. To achieve this goal, computer software needs to be trained on big data using emotion measures. These emotions should be elicited by a standardised, replicable and validated medium. However, collecting and rating such emotion elicitation media is not a trivial task, as it involves several factors. This research aims at designing a crowdsourcing platform to collect and rate emotion elicitation media. The platform is designed such that registered users can add, recommend and rate emotion election clips, whereas researchers can access and statically analyse data about the rated clips. This crowdsourcing platform can be used by emotion researchers to collect highly- rated emotion elicitation media, and by individuals through social media platform to share emotion elicitation media. The highly-rated clips could be used to elicit emotions, which then could be used to create models for automatic emotion recognition. The automation of emotion recognition will benefit different fields such as health (physical and mental), education and technology.