Question Quality Analysis and Prediction in Community Question Answering Services with Coupled Mutual Reinforcement

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Question Quality Analysis and Prediction in Community Question Answering Services with Coupled Mutual Reinforcement

Question Quality Analysis and Prediction in Community Question Answering Services with Coupled Mutual Reinforcement

Abstract

Community Question Answering Services java project report (CQAS) (e.g., Yahoo! Answers) provides a platform where people post questions and answer questions posed by others. Previous works analyzed the Answer Quality (AQ) based on answer-related features, but neglect the question-related features on AQ. Previous work analyzed how asker- and question-related features affect the Question Quality (QQ) regarding the amount of attention from users, the number of answers and the question solving latency, but neglect the correlation between QQ and AQ (measured by the rating of the best answer), which is critical to Quality of Service (QoS). We handle this problem from two aspects.

System Configuration:

H/W System Configuration:-

System             : I3 Processor.

Hard Disk          : 500 GB.

Monitor             : 15’’ LED

Input Devices    : Keyboard, Mouse

Ram                 : 4 GB

S/W System Configuration:-

Operating system    : Windows 7/UBUNTU.

Coding Language     : Java 1.7 ,Hadoop 0.8.1

IDE                        : Eclipse

Database                : MYSQL

Conclusion

In Community Question Answering Services paper, we conduct two studies to investigate question quality in CQA services. In Question Quality Analysis and Prediction in Community Question Answering Services with Coupled Mutual Reinforcement java project report study one, we analyze the factors influencing question quality and find that the interaction of users and topics leads to the difference of question quality.

Based on the findings of study one, in study two we propose a mutual reinforcement-based label propagation algorithm to predict question quality using features of question text and asker profile. We experiment with real world data set and the results demonstrate that our algorithm is more effective in distinguishing high quality questions from low quality ones than logistic regression model and other state-of-the-art algorithms, such as the stochastic gradient boosted tree and the harmonic function.

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Name of the Project   : Question Quality Analysis and Prediction in Community Question Answering Services with Coupled Mutual Reinforcement

Project Cost                : $ 50

Delivery Time             :  Within 48 hours

For Help Whatsapp    : +91 9481545735 or Email  info@partheniumprojects.com

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