Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142598
Title: Knowledge-oriented Hierarchical Neural Network for sentiment classification
Authors: Wang, Yanliu
Li, Pengfei
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Wang, Y., & Li, P. (2019). Knowledge-oriented Hierarchical Neural Network for sentiment classification. IOP Conference Series: Materials Science and Engineering, 646(1), 012023-. doi:10.1088/1757-899x/646/1/012023
Abstract: Sentiment classification aims to classify the sentimental polarities of given texts. Lexicon-based approaches utilize lexical resources to explore the opinions according to some specific rules, whose effectiveness strongly depends on the goodness of the lexical resources and the rules. Traditional machine-learning methods tightly rely on feature engineering and external NLP toolkits with unavoidable errors. Deep learning models strongly rely on a large amount of labelled data to train their numerous parameters, which often suffer from overfitting issue since it is difficult to obtain sufficient training data. To address the issues, we design a model that combines Knowledge-oriented Convolutional Neural Network (K-CNN) and bidirectional Gated Recurrent Neural Network (biGRU) in a hierarchical way for sentiment classification. Firstly K-CNN is used to capture the n-gram features in sentences. Sentiment word filters are constructed in the knowledge-oriented channel of K-CNN based on the linguistic knowledge from SentiWv ordNet, which can capture the sentiment lexicons and alleviate overfitting effectively. Then biGRU with attention mechanism is utilized to model the sequential relations between sentences and obtain the document-level representation based on the relevance of each sentence to the final sentiment classification. Experiments on two datasets show that our model outperforms other classical deep neural network models.
URI: https://hdl.handle.net/10356/142598
DOI: 10.1088/1757-899X/646/1/012023
Rights: © 2019 The Authors (published under licence by IOP Publishing Ltd). Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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