Annotation and indexing of video content basedon sentiment analysis

Abstract: Due to scientific advances in mobility and connectivity, digital media can be distributed to multiple platforms by streams and video on demand services. The abundance of video productions poses a problem in term sof storage, organization and cataloging. How the movies or TV-series should be sorted and retrieved is much dictated by user preferences,motivating proper indexing and an notation of video content. While movies tend to be described by keywords or genre, this thesis constitutesan attempt to automatically index videos, based on their semantics. Representing a video by the sentiment it invokes, would not only be more descriptive, but could also be used to compare movies directly based onthe actual content. Since filmmaking is biased by human perception,this project looks to utilize these characteristics for machine learning.The video is modeled as a sequence of shots, attempting to capture the temporal nature of the information. Sentiment analysis of videos has been used as labels in a supervised learning algorithm, namely a SVM using a string kernel. Besides the specifics of learning, the work of this thesis involve other relevant fields such a feature extraction and videosegmentation. The results show that there are patterns in video fit for learning; however the performance of the method is inconclusive due to lack of data. It would therefore be interesting to evaluate the approach further, using more data along with minor modifications.