Cross-Domain Sentiment Encoding through Stochastic Word Embedding

Hao, Yanbin, Mu, Tingting, Hong, Richang, Wang, Meng, Liu, Xueliang and Goulermas, John Y (2020) Cross-Domain Sentiment Encoding through Stochastic Word Embedding. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 32 (10). pp. 1909-1922.

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Official URL: http://dx.doi.org/10.1109/tkde.2019.2913379

Abstract

Sentiment analysis is an important topic concerning identification of feelings, attitudes, emotions and opinions from text. To automate such analysis, a large amount of example text needs to be manually annotated for model training. This is laborious and expensive, but the cross-domain technique is a key solution to reducing the cost by reusing annotated reviews across domains. However, its success largely relies on the learning of a robust common representation space across domains. In the recent years, significant effort has been invested to improve the cross-domain representation learning by designing increasingly more complex and elaborate model inputs and architectures. We support that it is not necessary to increase design complexity as this inevitably consumes more time in model training. Instead, we propose to explore the word polarity and occurrence information through a simple mapping and encode such information more accurately whilst managing lower computational costs. The proposed approach is unique and takes advantage of the stochastic embedding technique to tackle cross-domain sentiment alignment. Its effectiveness is benchmarked with over ten data tasks constructed from two review corpora and it is compared against ten classical and state-of-the-art methods.

Item Type:	Article
Uncontrolled Keywords:	Training, Task analysis, Computational modeling, Stochastic processes, Standards, Buildings, Computer science, Cross domain, sentiment classification, word, document embedding, similarity matrix, stochastic neighbor embedding
Depositing User:	Symplectic Admin
Date Deposited:	07 May 2019 07:41
Last Modified:	17 Mar 2024 18:19
DOI:	10.1109/TKDE.2019.2913379
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3039631