Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM-Reference-Cited by-同舟云学术

Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

Published:2018-10-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 26th ACM international conference on Multimedia
language:
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Author:

Chen Yuxiao¹,Yuan Jianbo¹,You Quanzeng²,Luo Jiebo¹

Affiliation:

1. University of Rochester, Rochester, NY, USA

2. Microsoft Research AI, Redmond, WA, USA

Funder

National Science Foundation

Goergen Institute of Data Science

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3240508.3240533

Reference48 articles.

1. Hanaa A. Aldahawi. 2015. Mining and analysing social network in the oil business: Twitter sentiment analysis and prediction approaches. Ph.D. Dissertation. Cardiff University UK. Hanaa A. Aldahawi. 2015. Mining and analysing social network in the oil business: Twitter sentiment analysis and prediction approaches. Ph.D. Dissertation. Cardiff University UK.

2. Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

3. Are emojis creating a new or old visual language for new generations? A socio-semiotic study;Alshenqeeti Hamza;Advances in Language and Literary Studies,2016

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