Lexicon-based sentiment convolutional neural networks for online review analysis

Minghui HUANG, Haoran XIE, Yanghui RAO, Yuwei LIU, Kin Man POON, Fu Lee WANG

Research output: Contribution to journalArticles

Abstract

With the growing availability and popularity of sentiment-rich resources like blogs and online reviews, new opportunities and challenges have emerged regarding the identification, extraction, and organization of sentiments from user-generated documents or sentences. Recently, many studies have exploited lexicon-based methods or supervised learning algorithms to separately conduct sentiment analysis tasks; however, the former approaches ignore contextual information of sentences and the latter ones do not take sentiment information embedded in sentiment words into consideration. To tackle these limitations, we propose a new model named Sentiment Convolutional Neural Network (SentiCNN) to analyze the sentiments of sentences with both contextual and sentiment information of sentiment words, in which, contextual information is captured from word embeddings and sentiment information is identified using existing lexicons. We incorporate a Highway Network into our model to adaptively combine sentiment and contextual information from sentences by strengthening the connection between features of both sentences and their sentiment words. Furthermore, we propose three lexicon-based attention mechanisms (LBAMs) for our SentiCNN model to find the most important indicators of sentiments and make predictions more effectively. Experiments over two well-known datasets indicate that sentiment words, the Highway Network, and LBAMs contribute to sentiment analysis. Copyright © 2020 IEEE.
Original languageEnglish
JournalIEEE Transactions on Affective Computing
Early online dateMay 2020
DOIs
Publication statusE-pub ahead of print - May 2020

Citation

Huang, M., Xie, H., Rao, Y., Liu, Y., Poon, L. K. M., & Wang, F. L. (2020). Lexicon-based sentiment convolutional neural networks for online review analysis. IEEE Transactions on Affective Computing. Advance online publication. doi: 10.1109/TAFFC.2020.2997769

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