The current state-of-the art in noise assessment involves the development of a strategic noise map to identify areas with excessive noise levels, expressed in terms of a single time-averaged noise indicator. While noise maps yield important information regarding sound pressure levels in a particular space, they do not give any representation of the overall sound quality in that space. A more human-centered approach to noise assessment could be achieved by developing soundscapes as a complementary tool to noise mapping. However, most soundscape studies traditionally use surveys or interviews to assess general sentiment toward the acoustic environment, and as such are generally restricted to small geographic areas, compared to the entire cities considered in noise mapping. Instead of using traditional assessment techniques, this project aims to harness the potential of big data, including, for example noise complaint data or social media chatter related to noise, to better assess public sentiments towards soundscapes. This would yield an unparalleled dataset of public opinions and perceptions of the acoustic environment. Initial results based on an analysis of NYC311 complaints and geo-localized data mined from Twitter are presented.