Twitter Sentiment Classification Based on Deep Random Vector Functional Link

Pablo A. Henriquez, Gonzalo A. Ruz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Social networking sites like Twitter, Facebook, Google + are rapidly gaining popularity as they allow people to share and express their views about topics, have discussion with different communities, or post messages across the world. There has been a lot of work in the field of sentiment analysis of Twitter data. Randomization based methods for training neural networks have gained increased attention in recent years achieving remarkable performances on a wide variety of tasks. The idea of randomly assigning neural network parameters is shared by different models like Random Vector Functional Link (RVFL) networks, the Liquid State Machine and the Feedforward Neural Network with Random Weights. We propose a novel non-iterative deep neural network using RVFL networks called Deep RVFL (D-RVFL). We evaluate the performance of D-RVFL using two Twitter datasets (the Catalan referendum of 2017 and the Chilean earthquake of 2010). In particular, we compare the classification performance of D-RVFL in human sentiment with Support Vector Machine (SVM), Random Forest, and the standard RVFL. The results confirm the advantages of using the proposed method for sentiment classification in Twitter in terms of the F1 score.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060146
DOIs
StatePublished - 10 Oct 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2018-July

Conference

Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
Country/TerritoryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

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