Abstract: Disclosed herein are methods and systems for digital media content search and recommendation. The system analyses large number of reviews and creates a metadata attributes which define particular media content. These attributes are based on experience of a large number of people and therefore are representative of a large audience. The system further uses these attributes to recommend media items based on users view history or based on users search parameters. According to the preferred embodiment, the method to execute the present invention is divided into three phases: training, tagging and search and recommendation phase. The training phase includes processing large number of text reviews of a wide range of movies to determine commonly talked about attributes and creates a global attribute dictionary. In tagging phase, each movie is tagged and classified based on the dictionaries and further movies are recommended depending on users search criterion and view history.