A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

Learners in a massive open online course often express feelings, exchange ideas and seek help by posting questions in discussion forums. Due to the very high learner-to-instructor ratios, it is unrealistic to expect instructors to adequately track the forums, find all of the issues that need resolution and understand their urgency and sentiment. In this paper, considering the biases among different courses, we propose a transfer learning framework based on a convolutional neural network and a long short-term memory model, called ConvL, to automatically identify whether a post expresses confusion, determine the urgency and classify the polarity of the sentiment. First, we learn the feature representation for each word by considering the local contextual feature via the convolution operation. Second, we learn the post representation from the features extracted through the convolution operation via the LSTM model, which considers the long-term temporal semantic relationships of features. Third, we investigate the possibility of transferring parameters from a model trained on one course to another course and the subsequent fine-tuning. Experiments on three real-world MOOC courses confirm the effectiveness of our framework. This work suggests that our model can potentially significantly increase the effectiveness of monitoring MOOC forums in real time.

[1]  Peng Hao,et al.  Transfer learning using computational intelligence: A survey , 2015, Knowl. Based Syst..

[2]  Aneesha Bakharia,et al.  Towards Cross-domain MOOC Forum Post Classification , 2016, L@S.

[3]  Lise Getoor,et al.  Understanding MOOC Discussion Forums using Seeded LDA , 2014, BEA@ACL.

[4]  Rui Xia,et al.  Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification , 2013, IEEE Intelligent Systems.

[5]  Jiashen Sun,et al.  Incorporating Domain and Sentiment Supervision in Representation Learning for Domain Adaptation , 2015, IJCAI.

[6]  Rui Yan,et al.  How Transferable are Neural Networks in NLP Applications? , 2016, EMNLP.

[7]  Yoshua Bengio,et al.  Unsupervised and Transfer Learning Challenge: a Deep Learning Approach , 2011, ICML Unsupervised and Transfer Learning.

[8]  Qun Liu,et al.  Encoding Source Language with Convolutional Neural Network for Machine Translation , 2015, ACL.

[9]  Danushka Bollegala,et al.  Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings , 2016, IEEE Transactions on Knowledge and Data Engineering.

[10]  Andreas Paepcke,et al.  YouEDU: Addressing Confusion in MOOC Discussion Forums by Recommending Instructional Video Clips , 2015, EDM.

[11]  Zhiyuan Liu,et al.  Neural Sentiment Classification with User and Product Attention , 2016, EMNLP.

[12]  Carolyn Penstein Rosé,et al.  Sentiment Analysis in MOOC Discussion Forums: What does it tell us? , 2014, EDM.

[13]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[14]  Shourya Roy,et al.  Cross-domain Text Classification with Multiple Domains and Disparate Label Sets , 2016, ACL.

[15]  Carolyn Penstein Rosé,et al.  Exploring the Effect of Confusion in Discussion Forums of Massive Open Online Courses , 2015, L@S.

[16]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[17]  Yoshua Bengio,et al.  Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.

[18]  Conrad S. Tucker,et al.  Knowledge Discovery of Student Sentiments in MOOCs and Their Impact on Course Performance , 2014 .

[19]  Jian-Yun Nie,et al.  Mining User Consumption Intention from Social Media Using Domain Adaptive Convolutional Neural Network , 2015, AAAI.

[20]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[21]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[22]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[23]  James H. Martin,et al.  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .

[24]  Qiang Yang,et al.  Cross-domain sentiment classification via spectral feature alignment , 2010, WWW '10.

[25]  John Blitzer,et al.  Domain Adaptation with Structural Correspondence Learning , 2006, EMNLP.

[26]  François Laviolette,et al.  Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..

[27]  Xuegang Hu,et al.  Domain adaptation via Multi-Layer Transfer Learning , 2016, Neurocomputing.

[28]  Xiaohua Hu,et al.  Linking Heterogeneous Input Features with Pivots for Domain Adaptation , 2015, IJCAI.

[29]  Zhi Liu,et al.  Sentiment recognition of online course reviews using multi-swarm optimization-based selected features , 2016, Neurocomputing.

[30]  Deniz Yuret,et al.  Transfer Learning for Low-Resource Neural Machine Translation , 2016, EMNLP.

[31]  Hongfei Lin,et al.  Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus , 2017, Algorithms.

[32]  Aurélien Garivier,et al.  On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models , 2014, J. Mach. Learn. Res..

[33]  Tim Menzies,et al.  Heterogeneous Defect Prediction , 2018, IEEE Trans. Software Eng..

[34]  Maayan Harel,et al.  Learning from Multiple Outlooks , 2010, ICML.

[35]  Timothy Baldwin,et al.  Named Entity Recognition for Novel Types by Transfer Learning , 2016, EMNLP.

[36]  Yoshua Bengio,et al.  Deep Learning of Representations for Unsupervised and Transfer Learning , 2011, ICML Unsupervised and Transfer Learning.

[37]  Haoran Xie,et al.  Cross-Domain Sentiment Classification via Topic-Related TrAdaBoost , 2017, AAAI.

[38]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[39]  Justin Reich,et al.  Forecasting student achievement in MOOCs with natural language processing , 2016, LAK.

[40]  Yifan Gong,et al.  Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[41]  Hui Chen,et al.  Knowledge-based document embedding for cross-domain text classification , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[42]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[43]  Omprakash Gnawali,et al.  Language independent analysis and classification of discussion threads in Coursera MOOC forums , 2014, Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014).

[44]  Xiaocheng Feng,et al.  Effective LSTMs for Target-Dependent Sentiment Classification , 2015, COLING.

[45]  Ralph Grishman,et al.  Relation Extraction: Perspective from Convolutional Neural Networks , 2015, VS@HLT-NAACL.

[46]  Luís A. Alexandre,et al.  Improving Deep Neural Network Performance by Reusing Features Trained with Transductive Transference , 2014, ICANN.

[47]  Danielle S. McNamara,et al.  Language to Completion: Success in an Educational Data Mining Massive Open Online Class , 2015, EDM.