چكيده به لاتين
By extremely increasing in the volume of documents and information, and on other hand representing them in digital format in the news, blogs, scientific articles, electronic books, photos, audio and video, and social networks, it’s difficult to find what we’re looking for in these digital archives. Therefore, there is a need for new computing tools to organize, search and understand this massive amount of information.
Topic modeling algorithms are statistical methods that analyze the words within a text, thus extracting the topics within the texts. It also identifies the relationship between these topics and their changes over time. These algorithms do not require any initial assumptions about the topic matter of texts or the labeling of texts. But their input is the original text. Topic modeling algorithms allow us to organize and summarize our electronic archives in such a way that it does not come from the point of view of human beings.
Topic modeling is a type of statistical model for discovering the latent ‘‘topics’’ that occur in a collection of documents through machine learning. Currently, latent Dirichlet allocation (LDA) is a popular and common modeling approach. In this research, we investigate the procedure of topic changes in scientific documents in the field of e-commerce using latent Dirichlet allocation algorithm. For this purpose, articles about the 10 years e- commerce published in authentic journals have been used.
