A Multichannel Convolutional Neural Network for Multilabel Sentiment Classification Using Abilify Oral User Reviews

Tina Esther Trueman, Ashok Kumar Jayaramn, Jasmine S, Gayathri Ananthakrishnan, Narayanasamy P

Abstract


Nowadays, patients and caregivers have become very active in social media. They are sharing a lot of information about their medication and drugs in terms of posts or comments. Therefore, sentiment analysis plays an active role to compute those posts or comments. However, each post is associated with multilabel such as ease of use, effectiveness, and satisfaction. To solve this kind of problem, we propose a multichannel convolution neural network for multilabel sentiment classification using Abilify oral user comments. The multichannel represents the multiple versions of the standard model with different strides. Specifically, we use the pre-trained model to generate word vectors. The proposed model is evaluated with multilabel metrics. The results indicate that the proposed multichannel convolutional network model outperforms the traditional machine learning algorithms.

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References


Feldman, R. (2013) ``Techniques and applications for sentiment analysis." Communications of the ACM, 56(4), 82-89.

Liu, B. (2020) ``Sentiment analysis: Mining opinions, sentiments, and emotions." Cambridge university press.

Tsoumakas, G., & Katakis, I. (2007) ``Multi-label classification: An overview." International Journal of Data Warehousing and Mining (IJDWM), 3(3), 1-13.

Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2009, September) ``Classifier chains for multi-label classification." In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 254-269). Springer, Berlin, Heidelberg.

Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010) ``Random k-labelsets for multilabel classification." IEEE Transactions on Knowledge and Data Engineering, 23(7), 1079-1089.

LeCun, Y., Bengio, Y., & Hinton, G. (2015) ``Deep learning." nature, 521(7553), 436-444.

Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016) ``Deep learning (Vol. 1, No. 2)." Cambridge: MIT press.

Pennington, J., Socher, R., & Manning, C. D. (2014, October) ``Glove: Global vectors for word representation." In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).

Baumel, T., Nassour-Kassis, J., Cohen, R., Elhadad, M., & Elhadad, N. (2017) ``Multi-label classification of patient notes a case study on ICD code assignment." arXiv preprint arXiv:1709.09587.

Wang, Y., Sohn, S., Liu, S., Shen, F., Wang, L., Atkinson, E. J., ... & Liu, H. (2019) ``A clinical text classification paradigm using weak supervision and deep representation." BMC medical informatics and decision making, 19(1), 1.

Singh, G., Thomas, J., Marshall, I. J., Shawe-Taylor, J., & Wallace, B. C. (2018) ``Structured multi-label biomedical text tagging via attentive neural tree decoding." arXiv preprint arXiv:1810.01468.

Citrome, L. (2006) ``A review of aripiprazole in the treatment of patients with schizophrenia or bipolar I disorder." Neuropsychiatric Disease and Treatment, 2(4), 427.

Rios, A., & Kavuluru, R. (2015, September) ``Convolutional neural networks for biomedical text classification: application in indexing biomedical articles." In Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (pp. 258-267).

Gargiulo, F., Silvestri, S., & Ciampi, M. (2018) ``Deep Convolution Neural Network for Extreme Multi-label Text Classification." In HEALTHINF (pp. 641-650).

Kolesov, A., Kamyshenkov, D., Litovchenko, M., Smekalova, E., Golovizin, A., & Zhavoronkov, A. (2014) ``On multilabel classification methods of incompletely labeled biomedical text data." Computational and mathematical methods in medicine.

Parwez, M. A., & Abulaish, M. (2019) ``Multi-label classification of microblogging texts using convolution neural network." IEEE Access, 7, 68678-68691.

Ashok Kumar J, Abirami S, Tina Esther Trueman. (October 13, 2020) ``Abilify Oral user reviews." IEEE Dataport, doi: https://dx.doi.org/10.21227/p1jp-2m84.

Kumar, J. A., Abirami, S., & Trueman, T. E. (2019, December) ``Multilabel Aspect-Based Sentiment Classification for Abilify Drug User Review." In 2019 11th International Conference on Advanced Computing (ICoAC) (pp. 376-380). IEEE.

Kim, Y. (2014) ``Convolutional neural networks for sentence classification." arXiv preprint arXiv:1408.5882.

Burkhardt, S., & Kramer, S. (2018) ``Online multi-label dependency topic models for text classification." Machine Learning, 107(5), 859-886.




DOI: https://doi.org/10.31449/inf.v47i1.3510

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