Abstract
Question answering (QA) websites now play a crucial role in meeting Internet users’ information needs. Quora is a growing QA platform where users get quick answers to their questions from their peers. Nonetheless, it is noted that a significant number of questions remained unanswered for a long time. Questions that have long been unable to receive any answer, opinion-based, need a debate to get the answers, or a valid answer does not exist, fall under Insincere question group. It is therefore important to weed out Insincere questions in order to maintain the integrity of the site. Quora have a huge number of such questions that can not be filtered manually. To overcome this problem, this paper proposes a multi-layer convolutional neural network model that helps to minimize Insincere questions from the website. Two embeddings were created from Quora dataset: (i) using Skipgram, and (ii) using Continuous Bag of Word model. The created embeddings and a pre-trained GloVe embedding vector were used for system development. The proposed model needs only the question text to predict the question is Insincere question or not and hence free from manual feature engineering. The experimental results indicated that the proposed multilayer CNN model outperforming over the earlier works by achieving the F1-score of 0.98 for the best case.
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References
Blooma MJ, Kurian JC (2011) Research issues in community based question answering. In: PACIS, pp 1–9
Roy PK, Singh JP, Baabdullah AM, Kizgin H, Rana NP (2018) Identifying reputation collectors in community question answering (CQA) sites: exploring the dark side of social media. Int J Inf Manag 42:25–35
Anderson A, Huttenlocher D, Kleinberg J, Leskovec J (2012) Discovering value from community activity on focused question answering sites: a case study of stack overflow. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 850–858
Paul SA, Hong L, Chi EH (2012) Who is authoritative? understanding reputation mechanisms in quora. pp 1–8. arXiv preprint arXiv:1204.3724
Guzmán F, Nakov P, Mà rquez L (2016) MTE-NN at SemEval-2016 task 3: can machine translation evaluation help community question answering? In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 887–895
Tian Y, Kochhar PS, Lim EP, Zhu F, Lo D (2013) Predicting best answerers for new questions: an approach leveraging topic modeling and collaborative voting. In: Workshops at the international conference on social informatics, Springer, Berlin, pp 55–68
Maity SK, Kharb A, Mukherjee A (2018) Analyzing the linguistic structure of question texts to characterize answerability in quora. IEEE Trans Comput Soc Syst 5(3):816–828
Wang G, Gill K, Mohanlal M, Zheng H, Zhao BY (2013) Wisdom in the social crowd: an analysis of quora. In: 22nd international world wide web conference, WWW ’13, Rio de Janeiro, Brazil, May 13–17, 2013, pp 1341–1352
Ahasanuzzaman M, Asaduzzaman M, Roy CK, Schneider KA (2016) Mining duplicate questions of stack overflow. In: IEEE/ACM 13th working conference on mining software repositories (MSR), 2016, IEEE, pp 402–412
Hoogeveen D, Bennett A, Li Y, Verspoor KM, Baldwin T (2018) Detecting misflagged duplicate questions in community question-answering archives. In: ICWSM, pp 112–120
Zhang WE, Sheng QZ, Lau JH, Abebe E, Ruan W (2018) Duplicate detection in programming question answering communities. ACM Trans Int Technol (TOIT) 18(3):37
Al-Ramahi M, Alsmadi I (2020) Using data analytics to filter insincere posts from online social networks a case study: quora insincere questions. In: Proceedings of the 53rd Hawaii international conference on system sciences, pp 2489–2497
Jain DK, Jain R, Upadhyay Y, Kathuria A, Lan X (2019) Deep refinement: capsule network with attention mechanism-based system for text classification. Neural Comput Appl 1–18
Mungekar A, Parab N, Nima P, Pereira S (2019) Quora insincere question classification. Natl College Irel 1–7
Priyambowo H, Adriani M (2019) Insincere question classification on question answering forum. In: 2019 International conference on electrical engineering and informatics (ICEEI), IEEE, pp 390–394
Gabbard S, Yang J, Liu J (2018) Quora insincere question classification. Baskin Engineering, University of California, Santa Cruz pp 1–6
Silva RF, Paixão K, de Almeida Maia M (2018) Duplicate question detection in stack overflow: a reproducibility study. In: 2018 IEEE 25th international conference on software analysis evolution and reengineering (SANER), IEEE, pp 572–581
Yih Wt, He X, Meek C (2014) Semantic parsing for single-relation question answering. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (Volume 2: Short Papers), vol 2, pp 643–648
Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12(Aug):2493–2537
Zhang Y, Lo D, Xia X, Sun JL (2015) Multi-factor duplicate question detection in stack overflow. J Comput Sci Technol 30(5):981–997
Roy PK, Ahmad Z, Singh JP, Alryalat MAA, Rana NP, Dwivedi YK (2018) Finding and ranking high-quality answers in community question answering sites. Glob J Flex Syst Manag 19(1):53–68
Wang XJ, Tu X, Feng D, Zhang L (2009) Ranking community answers by modeling question–answer relationships via analogical reasoning. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval, ACM, pp 179–186
Blooma MJ, Chua AYK, Goh DHL (2010) Selection of the best answer in CQA services. In: Seventh international conference on information technology: new generations (ITNG), 2010, IEEE, pp 534–539
Patil S, Lee K (2016) Detecting experts on quora: by their activity, quality of answers, linguistic characteristics and temporal behaviors. Soc Netw Anal Min 6(1):1–25
Abishek K, Hariharan BR, Valliyammai C (2019) An enhanced deep learning model for duplicate question pairs recognition. In: Soft computing in data analytics, Springer, Berlin, pp 769–777
Mueller J, Thyagarajan A (2016) Siamese recurrent architectures for learning sentence similarity. In: Thirtieth AAAI conference on artificial intelligence, pp 2786–2792
Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543
Saedi C, Rodrigues J, Silva J, Branco A, Maraev V (2017) Learning profiles in duplicate question detection. In: 2017 IEEE international conference on information reuse and integration (IRI), pp 544–550. https://doi.org/10.1109/IRI.2017.39
Bacchelli A (2013) Dataset: mining challenge 2013: “mining challenge 2013: stack overflow”. In: 10th international conference on mining software repositories (MSR)
Ying AT (2015) Mining challenge 2015: comparing and combining different information sources on the stack overflow data set. In: The 12th working conference on mining software repositories
Dror G, Maarek Y, Szpektor I (2013) Will my question be answered? predicting “question answerability” in community question-answering sites. In: Blockeel H, Kersting K, Nijssen S, Železný F (eds) Machine learning and knowledge discovery in databases. Springer, Heidelberg, pp 499–514
Yang L, Bao S, Lin Q, Wu X, Han D, Su Z, Yu Y (2011) Analyzing and predicting not-answered questions in community-based question answering services. In: AAAI, pp 1273–1278
Srba I, Bielikova M (2016) Why is stack overflow failing? preserving sustainability in community question answering. IEEE Softw 33(4):80–89
Wang G, Gill K, Mohanlal M, Zheng H, Zhao BY (2013) Wisdom in the social crowd: an analysis of quora. In: Proceedings of the 22nd international conference on world wide web, ACM, pp 1341–1352
Gaire B, Rijal B, Gautam D, Sharma S, Lamichhane N (2019) Insincere question classification using deep learning. Int J Sci Eng Res 10:2001–2004
Singh JP, Irani S, Rana NP, Dwivedi YK, Saumya S, Roy PK (2017) Predicting the “helpfulness” of online consumer reviews. J Bus Res 70:346–355
Saumya S, Singh JP, Dwivedi YK (2019) Predicting the helpfulness score of online reviews using convolutional neural network. Soft Comput 1–17
Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. pp 1–11. arXiv preprint arXiv:1404.2188
Lee Y, Chung M, Cho S, Choi J (2019) Extraction of product evaluation factors with a convolutional neural network and transfer learning. Neural Process Lett 1–16
Kim Y (2014) Convolutional neural networks for sentence classification. pp 1–6. arXiv preprint arXiv:1408.5882
Sadr H, Pedram MM, Teshnehlab M (2019) A robust sentiment analysis method based on sequential combination of convolutional and recursive neural networks. Neural Process Lett 1–17
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Wen S, Liu W, Yang Y, Zhou P, Guo Z, Yan Z, Chen Y, Huang T (2020) Multilabel image classification via feature/label co-projection. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/TSMC.2020.2967071
Wen S, Dong M, Yang Y, Zhou P, Huang T, Chen Y (2019a) End-to-end detection-segmentation system for face labeling. IEEE Trans Emerg Top Comput Intell 1–11
Wen S, Wei H, Yan Z, Guo Z, Yang Y, Huang T, Chen Y (2019b) Memristor-based design of sparse compact convolutional neural network. IEEE Transactions on Network Science and Engineering pp 1–11
Zhang Y, Zhao D, Sun J, Zou G, Li W (2016) Adaptive convolutional neural network and its application in face recognition. Neural Process Lett 43(2):389–399
Kumar A, Singh JP (2019) Location reference identification from tweets during emergencies: a deep learning approach. Int J Disas Risk Reduct 33:365–375
Roy PK, Singh JP (2019) Predicting closed questions on community question answering sites using convolutional neural network. Neural Comput Appl 1–18
Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT’2010, Springer, Berlin, pp 177–186
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 1189–1232
Rish I (2001) An empirical study of the naive bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, IBM, pp 41–46
Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin
Yan Z, Piramuthu R, Jagadeesh V, Di W, Decoste D (2019) Hierarchical deep convolutional neural network for image classification. US Patent 10,387,773
Hassan J, Shoaib U (2019) Multi-class review rating classification using deep recurrent neural network. Neural Process Lett 1–18
Roy PK, Singh JP, Banerjee S (2020) Deep learning to filter sms spam. Future Gener Comput Syst 102:524–533
Ponzanelli L, Mocci A, Bacchelli A, Lanza M, Fullerton D (2014) Improving low quality stack overflow post detection. In: IEEE international conference on software maintenance and evolution (ICSME), 2014, IEEE, pp 541–544
Mizobuchi Y, Takayama K (2017) Two improvements to detect duplicates in stack overflow. In: IEEE 24th international conference on software analysis, evolution and reengineering (SANER), 2017, IEEE, pp 563–564
Zhang WE, Sheng QZ, Lau JH, Abebe E (2017a) Detecting duplicate posts in programming qa communities via latent semantics and association rules. In: Proceedings of the 26th international conference on world wide web, international world wide web conferences steering committee, pp 1221–1229
Zhang WE, Sheng QZ, Shu Y, Nguyen VK (2017b) Feature analysis for duplicate detection in programming qa communities. In: International conference on advanced data mining and applications, Springer, Berlin, pp 623–638
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Roy, P.K. Multilayer Convolutional Neural Network to Filter Low Quality Content from Quora. Neural Process Lett 52, 805–821 (2020). https://doi.org/10.1007/s11063-020-10284-x
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DOI: https://doi.org/10.1007/s11063-020-10284-x