A Review of Sentiment Analysis Research in Chinese Language

By Haiyun Peng, Erik Cambria, Amir Hussain. 2017

183 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network (2018, methodology): “Peng et al. [21] developed a system which can provide invaluable added value to the government, business owners and Internet users by analyzing the sentiment element from countless amounts of new information entries each day through product descriptions, customer reviews.”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “(Further details on Chinese sentiment analysis can be found in this survey article by Peng, Cambria, and Hussain (2017); Further details on Arabic sentiment analysis can be found in these survey articles by Al-Ayyoub, Khamaiseh, Jararweh, and Al-Kabi (2019), Alowisheq, Alhumoud, Altwairesh, and…”
A survey on classification techniques for opinion mining and sentiment analysis (2017, background): “Usingdifferent approaches and combining themat the conceptual level can be further investigated in the future (Bisio et al. 2017; Bajpai et al. 2017; Peng et al. 2017; Cambria 2016; Jain and Jain 2017; Qazi et al. 2016).”
Radical-Based Hierarchical Embeddings for Chinese Sentiment Analysis at Sentence Level (2017, background): “However, deeper semantic information and clues could be found at radical-level analysis (Peng, Cambria, and Hussain 2017).”
Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review (2019, background): “37% (3-way) 29 Peng et al. (2018) Notebook Chinese aspect Chinese Aspect target sequence model (ATSM), working Acc: 75.”