Abstract
Technology question and answer websites are a great source of technical knowledge. Users of these websites raise various types of technical questions, and answer them. These questions cover a wide range of domains in Computer Science like Networks, Data Mining, Multimedia, Multi-threading, Web Development, Mobile App Development, etc. Analyzing the actual textual content of these websites can help computer science and software engineering community better understand the needs of developers and learn about the current trends in technology. In this project, textual data from famous question and answer website called StackOverflow, is analyzed using Latent Dirichlet Allocation (LDA) topic modeling algorithm. The results show that this techniques help discover dominant topics in developer discussions. These topics are analyzed to find a number of interesting observations such as popular technology/language, impact of a technology, technology trends over time, relationship of a technology/language with other technologies and comparison of technologies addressing an area of computer science or software engineering.
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