Programming question and answer (Q & A) websites, such as Quora, Stack Overflow, and Yahoo! Answer etc. helps us to understand the programming concepts easily and quickly in a way that has been tested and applied by many software developers. Stack Overflow is one of the most frequently used programming Q &A website where the questions and answers posted are presently analyzed manually, which requires a huge amount of time and resource. To save the effort, we present a topic modeling based technique to analyze the words of the original texts to discover the themes that run through them. We also propose a method to automate the process of reviewing the quality of questions on Stack Overflow dataset in order to avoid ballooning the stack overflow with insignificant questions. The proposed method also recommends the appropriate tags for the new post, which averts the creation of unnecessary tags on Stack Overflow.
: Previous work has focused on analyzing general Q &A websites based on user's social interactions. @cite_0 have analyzed several aspects of user behavior in Yahoo! Answers, a Q &A website for the general public. The authors use the number of questions and answers in each predefined top level category to determine the popularity of each category. @cite_6 have also analyzed Yahoo! Answers to cluster the top-level categories into three broader categories using both content and user interactions. In contrast to these efforts, instead of using existing tags, we use a statistical topic model, LDA, to automatically discover topics from the textual content of the posts and employ temporal measures to identify a topic's popularity over time.
@cite_0 @cite_6
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Yahoo! Answers represents a new type of community portal that allows users to post questions and or answer questions asked by other members of the community, already featuring a very large number of questions and several million users. Other recently launched services, like Microsoft’s Live QnA and Amazon’s Askville, follow the same basic interaction model. The popularity and the particular characteristics of this model call for a closer study that can help a deeper understanding of the entities involved, their interactions, and the implications of the model. Such understanding is a crucial step in social and algorithmic research that could yield improvements to various components of the service, for instance, personalizing the interaction with the system based on user interest. In this paper, we perform an analysis of 10 months worth of Yahoo! Answers data that provides insights into user behavior and impact as well as into various aspects of the service and its possible evolution. Yahoo Answers (YA) is a large and diverse question-answer forum, acting not only as a medium for sharing technical knowledge, but as a place where one can seek advice, gather opinions, and satisfy one's curiosity about a countless number of things. In this paper, we seek to understand YA's knowledge sharing and activity. We analyze the forum categories and cluster them according to content characteristics and patterns of interaction among the users. While interactions in some categories resemble expertise sharing forums, others incorporate discussion, everyday advice, and support. With such a diversity of categories in which one can participate, we find that some users focus narrowly on specific topics, while others participate across categories. This not only allows us to map related categories, but to characterize the entropy of the users' interests. We find that lower entropy correlates with receiving higher answer ratings, but only for categories where factual expertise is primarily sought after. We combine both user attributes and answer characteristics to predict, within a given category, whether a particular answer will be chosen as the best answer by the asker.