NDLI: Topical Authoritative Answerer Identification on Q&A Posts using Supervised Learning in CQA Sites

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Topical Authoritative Answerer Identification on Q&A Posts using Supervised Learning in CQA Sites

Content Provider	ACM Digital Library
Author	Nagwani, Naresh Kumar Sahu, Tirath Prasad Verma, Shrish
Abstract	Community Question Answering (CQA) site is an online platform for hosting information in question-answer form by collaborative users worldwide. There are basically two types of user in this CQA sites: Asker -- who post their query as questions and Answerer -- who provide the answers to these questions. The semi-structured and growing size of contents in CQA sites is posing several challenges. As there is no restriction in posting the number of answers to a question, so the common challenge is to identify the authoritative answerers of a question in order to evaluate the answer quality for selecting the best answer. In this paper, we use latent dirichlet allocation (LDA) the statistical topic modelling on textual data and statistical computing on metadata to identify the features that would reflect the topical authoritative of answerer. Then these features are represented as vector for each answerer of the dataset under investigation for learning the classifier model. The various baseline classifier model are used to identify the topical authoritative answerer on Q&A posts of two real dataset extracted from StackOverflow and AskUbuntu. The correctness and effectiveness of classifier models are evaluated using various parameters like accuracy, precision, recall, and kappa statistic. The experimental result shows that Random Forest classifier outperforms over each evaluation parameter than other classification algorithms.
Starting Page	129
Ending Page	132
Page Count	4
File Format	PDF
ISBN	9781450348089
DOI	10.1145/2998476.2998490
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Feature identification Topic modeling Cqa Authority users Classification Statistical computing
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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