A lightweight algorithm for the emotional classification of crowdsourced venue reviews

Finding emotions in text is an area of research with wide-ranging applications. Analysis of sentiment in text can help determine the opinions and affective intent of writers, as well as their attitudes, evaluations and inclinations with respect to various topics. Previous work in sentiment analysis has been done on a variety of text genres, including product and movie reviews, news stories, editorials and opinion articles, or blogs. We describe a lightweight emotion annotation algorithm for identifying emotion category & intensity in reviews written by social media (Foursquare) users. The algorithm is evaluated against human subject performance and is found to compare favourably. This work opens up opportunities for solving the problem of helping user navigate through the plethora of venue reviews in mobile and desktop applications.