research-article

Deep learning for automated sentiment analysis of social media

Authors:

Song-Lin TsaiAuthors Info & Claims

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 1001 - 1004

https://doi.org/10.1145/3341161.3344821

Published: 15 January 2020 Publication History

Abstract

The spread of information on Facebook and Twitter is much more efficient than on traditional social media platforms. For word-of-mouth (WOM) marketing, social media have become a rich information source for companies or scholars to design models to examine this repository and mine useful insights for marketing strategies. However, social media language is relatively short and contains special words and symbols. Most natural language processing (NLP) methods focus on processing formal sentences and are not well-suited to such short messages. In this study we propose a novel sentiment analysis framework based on deep learning models to extract sentiment from social media. We collect data from which we compile a dataset. After processing these special terms, we seek to establish a semantic dataset for further research. The extracted information will be useful for many future applications. The experimental data have been obtained by crawling several social media platforms.

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Cited By

Lora SAhmed IAdnan M(2024)Short Paper: A Cloud-based Distributed Approach for Social Media Sentiment Analysis using Machine Learning with Distributed Hyperparameter TuningProceedings of the 11th International Conference on Networking, Systems, and Security10.1145/3704522.3704548(240-246)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3704522.3704548
Kulkarni MNarvekar CGupta SKulkarni MDahat Y(2024)Use of Talos for Sentiment Analysis Using Hybrid LSTM-CNN Architecture2024 Second International Conference on Advances in Information Technology (ICAIT)10.1109/ICAIT61638.2024.10690680(1-4)Online publication date: 24-Jul-2024
https://doi.org/10.1109/ICAIT61638.2024.10690680
I V CM R S(2024)An Analysis and Detection of Depression from Textual Data using Deep Learning Methods2024 Second International Conference on Advances in Information Technology (ICAIT)10.1109/ICAIT61638.2024.10690434(1-5)Online publication date: 24-Jul-2024
https://doi.org/10.1109/ICAIT61638.2024.10690434
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Deep learning for automated sentiment analysis of social media

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cover image ACM Conferences

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2019

1228 pages

ISBN:9781450368681

DOI:10.1145/3341161

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

Copyright © 2019 ACM.

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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Association for Computing Machinery

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Published: 15 January 2020

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Ministry of Science and Technology

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ASONAM '19

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SIGKDD

ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining

August 27 - 30, 2019

British Columbia, Vancouver, Canada

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ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;

Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Lora SAhmed IAdnan M(2024)Short Paper: A Cloud-based Distributed Approach for Social Media Sentiment Analysis using Machine Learning with Distributed Hyperparameter TuningProceedings of the 11th International Conference on Networking, Systems, and Security10.1145/3704522.3704548(240-246)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3704522.3704548
Kulkarni MNarvekar CGupta SKulkarni MDahat Y(2024)Use of Talos for Sentiment Analysis Using Hybrid LSTM-CNN Architecture2024 Second International Conference on Advances in Information Technology (ICAIT)10.1109/ICAIT61638.2024.10690680(1-4)Online publication date: 24-Jul-2024
https://doi.org/10.1109/ICAIT61638.2024.10690680
I V CM R S(2024)An Analysis and Detection of Depression from Textual Data using Deep Learning Methods2024 Second International Conference on Advances in Information Technology (ICAIT)10.1109/ICAIT61638.2024.10690434(1-5)Online publication date: 24-Jul-2024
https://doi.org/10.1109/ICAIT61638.2024.10690434
Choudhary DGupta ASingh SAbhinav TAgrawal T(2024)Sentiment Analysis for Depression Detection Using Artificial Intelligence2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT)10.1109/AIIoT58432.2024.10574749(1-5)Online publication date: 3-May-2024
https://doi.org/10.1109/AIIoT58432.2024.10574749
Sharma NAli AKabir M(2024)A review of sentiment analysis: tasks, applications, and deep learning techniquesInternational Journal of Data Science and Analytics10.1007/s41060-024-00594-xOnline publication date: 1-Jul-2024
https://doi.org/10.1007/s41060-024-00594-x
Hasan FRaza DMoon HNahid M(2024)Sentiment Analysis from YouTube Video Using Bi-LSTM-GRU ClassificationProceedings of the 2nd International Conference on Big Data, IoT and Machine Learning10.1007/978-981-99-8937-9_21(303-316)Online publication date: 30-Mar-2024
https://doi.org/10.1007/978-981-99-8937-9_21
Shwetha CPushpalatha K(2024)Depression Detection from a Social Media Dataset Using Deep Learning and NLP Techniques: A ReviewICT for Intelligent Systems10.1007/978-981-97-6675-8_43(517-527)Online publication date: 29-Oct-2024
https://doi.org/10.1007/978-981-97-6675-8_43
Manoharan GJyothi BBhat RAcharjee PSharma ARawal P(2024)Utilizing Machine Learning for Advanced Natural Language Processing and Sentiment Analysis in Social Media PlatformsProceedings of Third International Conference in Mechanical and Energy Technology10.1007/978-981-97-2716-2_12(111-125)Online publication date: 3-Aug-2024
https://doi.org/10.1007/978-981-97-2716-2_12
Barroso-Moreno CRayon-Rumayor LGarcía-Vera A(2023)Big Data and Business Intelligence on Twitter and Instagram for digital inclusionComunicar10.3916/C74-2023-0431:74(49-60)Online publication date: 1-Jan-2023
https://doi.org/10.3916/C74-2023-04
Shchoholiev MAndriichuk O(2023)Mathematical Methods of Natural Language Processing in the System of Operative Determination of the Level of Tension in SocietyCybernetics and Computer Technologies10.34229/2707-451X.23.2.6(55-68)Online publication date: 18-Aug-2023
https://doi.org/10.34229/2707-451X.23.2.6
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