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Twitter Sentiment Analysis with Deep Convolutional Neural Networks

Published: 09 August 2015 Publication History

Abstract

This paper describes our deep learning system for sentiment analysis of tweets. The main contribution of this work is a new model for initializing the parameter weights of the convolutional neural network, which is crucial to train an accurate model while avoiding the need to inject any additional features. Briefly, we use an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning model on a distant supervised corpus. At a final stage, the pre-trained parameters of the network are used to initialize the model. We train the latter on the supervised training data recently made available by the official system evaluation campaign on Twitter Sentiment Analysis organized by Semeval-2015. A comparison between the results of our approach and the systems participating in the challenge on the official test sets, suggests that our model could be ranked in the first two positions in both the phrase-level subtask A (among 11 teams) and on the message-level subtask B (among 40 teams). This is an important evidence on the practical value of our solution.

References

[1]
A. Go, R. Bhayani, and L. Huang. Twitter sentiment classification using distant supervision. In CS224N Project Report, Stanford, 2009.
[2]
N. Kalchbrenner, E. Grefenstette, and P. Blunsom. A convolutional neural network for modelling sentences. In ACL, 2014.
[3]
Y. Kim. Convolutional neural networks for sentence classification. In EMNLP, 2014.
[4]
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS, 2013.
[5]
S. M. Mohammad, S. Kiritchenko, and X. Zhu. Nrc-canada: Building the state-of-the-art in sentiment analysis of tweets. In Semeval, 2013.
[6]
V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, 2010.
[7]
J. W. Ronan Collobert. A unified architecture for natural language processing: deep neural networks with multitask learning. In ICML, 2008.
[8]
Shen, He, Gao, Deng, and Mesnil}stuff:cikm:2014Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil. A latent semantic model with convolutional-pooling structure for information retrieval. CIKM, 2014.
[9]
Shen, He, Gao, Deng, and Mesnil}stuff:www:2014Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil. Learning semantic representations using convolutional neural networks for web search. In WWW, 2014.
[10]
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15: 1929--1958, 2014.
[11]
S. M. M. Xiaodan Zhu, Svetlana Kiritchenko. Nrc-canada-2014: Recent improvements in sentiment analysis of tweets, and the Voted Perceptron. In SemEval, 2014.
[12]
M. D. Zeiler. Adadelta: An adaptive learning rate method. CoRR, 2012.

Cited By

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  • (2024)BERT for Twitter Sentiment Analysis: Achieving High Accuracy and Balanced PerformanceJournal of Trends in Computer Science and Smart Technology10.36548/jtcsst.2024.1.0036:1(37-50)Online publication date: Mar-2024
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cover image ACM Conferences
SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
August 2015
1198 pages
ISBN:9781450336215
DOI:10.1145/2766462
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 09 August 2015

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Author Tags

  1. convolutional neural networks
  2. twitter sentiment analysis

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  • Short-paper

Funding Sources

  • Google Europe Doctoral Fellowship Award 2013
  • CogNet (H2020-ICT-2014-2)

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SIGIR '15
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SIGIR '15 Paper Acceptance Rate 70 of 351 submissions, 20%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2025)MPBE: Multi-perspective boundary enhancement network for aspect sentiment triplet extractionApplied Intelligence10.1007/s10489-024-06144-z55:4Online publication date: 13-Jan-2025
  • (2024)An Ensemble Approach to Enhance the Efficacy of Sentiment PredictionInternational Journal of Computer Theory and Engineering10.7763/IJCTE.2024.V16.135416:2(55-65)Online publication date: 2024
  • (2024)BERT for Twitter Sentiment Analysis: Achieving High Accuracy and Balanced PerformanceJournal of Trends in Computer Science and Smart Technology10.36548/jtcsst.2024.1.0036:1(37-50)Online publication date: Mar-2024
  • (2024)Analysis of the Citizen Social Well-Being: Correlation between Urban Public Health Infrastructure and Tonality of Texts from Social Networks (based on the Example of St. Petersburg)NSU Vestnik. Series: Linguistics and Intercultural Communication10.25205/1818-7935-2024-22-1-50-6422:1(50-64)Online publication date: 26-Jun-2024
  • (2024)Happiness and Sadness in Adolescents’ Instagram Direct Messaging: A Neural Topic Modeling ApproachSocial Media + Society10.1177/2056305124122965510:1Online publication date: 23-Feb-2024
  • (2024)MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market PredictionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679653(2379-2389)Online publication date: 21-Oct-2024
  • (2024)Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet ExtractionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657734(619-629)Online publication date: 10-Jul-2024
  • (2024)Social Media Sentiment Analysis using Deep Learning: Review2024 International Conference on Signal Processing and Advance Research in Computing (SPARC)10.1109/SPARC61891.2024.10828926(1-6)Online publication date: 12-Sep-2024
  • (2024)Real-time Twitter data sentiment analysis to predict the recession in the UK using Graph Neural Networks2024 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC61514.2024.10592592(1595-1600)Online publication date: 27-May-2024
  • (2024)A Comparison Between Transformers and Foundation Models in Sentiment Analysis of Student Evaluation of Teaching2024 12th International Symposium on Digital Forensics and Security (ISDFS)10.1109/ISDFS60797.2024.10527264(1-7)Online publication date: 29-Apr-2024
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