Influence of noise on transfer learning in Chinese sentiment classification using GRU

Mingjun Dai, Shansong Huang, Junpei Zhong, Chenguang Yang, Shiwei Yang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Sentiment classification for product reviews is of great significance for business feedback for manufactures, sellers and users. However, since a large amount of training data for a specific product domain is not always available, transfer learning is often utilized to do sentiment analysis applications. Specifically, after a pre-training of the large Chinese corpus by a word-embedding method, a larger size of training data for a specific domain was trained using a Gated Recurrent Unit. And then the trained model was used for testing the sentiment classification for a smaller amount of product reviews. The performances of this transfer learning method was also examined, especially to testify different factors affecting the performance of the transfer learning. The experimental results showed that different wording in the review domain (which we call it 'noise') will have a greater impact on transfer learning. We also calculate the difference of the wording to verify our hypothesis. According to these results, we have explored the impacts of the dataset wording, while we are doing Chinese text sentiment classification. We also shed a light in optimizing the transfer learning effect in general.

Original languageEnglish
Title of host publicationICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
EditorsLiang Zhao, Lipo Wang, Guoyong Cai, Kenli Li, Yong Liu, Guoqing Xiao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1844-1849
Number of pages6
ISBN (Electronic)9781538621653
DOIs
Publication statusPublished - 21 Jun 2018
Externally publishedYes
Event13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017 - Guilin, Guangxi, China
Duration: 29 Jul 201731 Jul 2017

Publication series

NameICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

Conference

Conference13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
Country/TerritoryChina
CityGuilin, Guangxi
Period29/07/1731/07/17

Keywords

  • Gated Recurrent Unit
  • neural network
  • sentiment classification
  • transfer learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Logic
  • Modelling and Simulation
  • Statistics and Probability

Cite this