Lexicon-Based Sentiment Convolutional Neural Networks for Online Review Analysis

Minghui HUANG, Haoran XIE*, Yanghui RAO, Yuwei LIU, Leonard K. M. POON, Fu Lee WANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

28 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1337-1348
Number of pages12
JournalIEEE Transactions on Affective Computing
Issue number3
Early online date28 May 2020
Publication statusPublished - Jul 2022

Bibliographical note

The research described in this article has been supported by the Interdisciplinary Research Scheme of the Dean’s Research Fund 2018-19 (FLASS/DRF/IDS-3), Departmental Collaborative Research Fund 2019 (MIT/DCRF-R2/18-19) of The Education University of Hong Kong, HKIBS Research Seed Fund 2019/20 (190-009) and Research Seed Fund (102367) of Lingnan University, Hong Kong.


  • Analytical models
  • Convolutional neural network
  • Feature extraction
  • Neural networks
  • Road transportation
  • Sentiment analysis
  • Supervised learning
  • Task analysis
  • attention mechanism
  • sentiment analysis
  • sentiment lexicon


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