With the development of digital media and other technologies, barrage comments, a new type of commentary system have become more and more popular. It enables audiences to immediately comment on videos and helps them understand the content. Barrage comments open up a new study area in short text and realtime data processing. By studying barrage comments deeply, we can understand the video plot; by studying the similarity between barrage comments and analyzing the association between users, we are able to understand the features of the users and potential connections between different videos, which can also provide a more accurate solution to the selection of target audience at the time of video production. We first introduce the collection and preprocessing on barrage comments, and then calculate the emotional values. Since the barrage comments are usually oral and out of structure in syntax and grammar, a dictionary for the commonly used barrage comments is built. The classic kmeans is adapted for obtaining the user groups based on the emotional values. We perform emotionbased classification for all users who post barrage comments. This sort of classification can help us understand the emotional similarities and differences among viewers watching a particular type of videos.