Multi-modal Topic Modelling and Summarization with Dense Block Detection: A Review

摘要

There has been incredible growth of events over the internet in recent years. Google has become the giant source of knowledge for any event which has happened or happening over the internet. Some networking sites such as face book, micro blogging sites such as twitter are evolved with time and became the highly used sites over the internet. Various E-commerce websites such as Amazon, Ebay, Flipkart etc are the widely used sites for online shopping. These sites generates large amount of text data. In association with text data some images are also uploaded over the internet on these sites. To model this huge amount of multi-modal data having both textual and visual contents multi-modal topic model for summarization, analysis is suggested in this paper. While dealing with multimodality, study of semantic relationship between the images and text data is crucial part. This model also helps to study semantic relationship between them effectively. Topics which are trending, popular over the world can be seen on Social sites as well as micro blogging sites. In online shopping sites fake reviews, advertises, spam spreading information is posted. For summarizing and analyzing the data we have taken the dataset containing reviews and product information from Amazon, one of the leading E-commerce sites. This information is used for modeling topic on sites with summarization and analysis. In this paper detailed study of other previous methods is also shown.