A novel meta-embedding technique for drug reviews sentiment analysis

Aye Hninn Khine, Wiphada Wettayaprasit, Jarunee Duangsuwan


Traditional word embedding models have been used in the feature extraction process of deep learning models for sentiment analysis. However, these models ignore the sentiment properties of words while maintaining the contextual relationships and have inadequate representation for domainspecific words. This paper proposes a method to develop a meta embedding model by exploiting domain sentiment polarity and adverse drug reaction (ADR) features to render word embedding models more suitable for medical sentiment analysis. The proposed lexicon is developed from the medical blogs corpus. The polarity scores of the existing lexicons are adjusted to assign new polarity score to each word. The neural network model utilizes sentiment lexicons and ADR in learning refined word embedding. The refined embedding obtained from the proposed approach is concatenated with original word vectors, lexicon vectors, and ADR feature to form a meta-embedding model which maintains both contextual and sentimental properties. The final meta-embedding acts as a feature extractor to assess the effectiveness of the model in drug reviews sentiment analysis. The experiments are conducted on global vectors (GloVE) and skip-gram word2vector (Word2Vec) models. The empirical results demonstrate the proposed meta-embedding model outperforms traditional word embedding in different performance measures.


Convolutional neural network; Global vectors; Multilayer perceptron; Natural language processing; Sentiment analysis; Word embedding; Word2vector

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DOI: http://doi.org/10.11591/ijai.v12.i4.pp1938-1946


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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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