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.

References

[1]

B. J. S. l. o. h. l. t. Liu, "Sentiment analysis and opinion mining," vol. 5, no. 1, pp. 1--167, 2012.

[2]

M. Allahyari, S. Pouriyeh, M. Assefi, S. Safaei, E. D. Trippe, J. B. Gutierrez, and K. J. a. p. a. Kochut, "A brief survey of text mining: Classification, clustering and extraction techniques," 2017.

[3]

F. M. Kundi, S. Ahmad, A. Khan, and M. Z. Asghar, "Detection and scoring of internet slangs for sentiment analysis using SentiWordNet," Life Science Journal, vol. 11, no. 9, pp. 66--72, 2014.

[4]

N. A. M. Zamani, S. Z. Abidin, N. Omar, and M. Abiden, "Sentiment analysis: Determining people's emotions in facebook." pp. 111--116.

[5]

L.-C. Cheng, P.-Y. Li, and S.-H. Chen, "Explore users' preference from Facebook fan pages." pp. 710--712.

[6]

A. Esuli, and F. Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining." pp. 417--422.

[7]

A. Hamouda, and M. Rohaim, "Reviews classification using sentiwordnet lexicon." pp. 104--105.

[8]

G. Fei, B. Liu, M. Hsu, M. Castellanos, and R. J. P. o. C. P. Ghosh, "A dictionary-based approach to identifying aspects implied by adjectives for opinion mining," pp. 309--318, 2012.

[9]

J. K. Rout, K.-K. R. Choo, A. K. Dash, S. Bakshi, S. K. Jena, and K. L. J. E. C. R. Williams, "A model for sentiment and emotion analysis of unstructured social media text," vol. 18, no. 1, pp. 181--199, 2018.

[10]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks." pp. 1097--1105.

[11]

Y. Gupta, P. J. J. I.-I. J. o. I. C. Kumar, and C. Technology, "CASAS: Customized Automated Sentiment Analysis System," vol. 5, no. 1, pp. 275--279, 2017.

[12]

S. Zhang, D. Zheng, X. Hu, and M. Yang, "Bidirectional long short-term memory networks for relation classification." pp. 73--78.

[13]

R. Dey, and F. M. Salemt, "Gate-variants of gated recurrent unit (GRU) neural networks." pp. 1597--1600.

[14]

E. Kouloumpis, T. Wilson, and J. D. Moore, "Twitter sentiment analysis: The good the bad and the omg!," Icwsm, vol. 11, no. 538-541, pp. 164, 2011.

[15]

A. Chatterjee, U. Gupta, M. K. Chinnakotla, R. Srikanth, M. Galley, and P. J. C. i. H. B. Agrawal, "Understanding Emotions in Text Using Deep Learning and Big Data," vol. 93, pp. 309--317, 2019.

[16]

"Natural Language Toolkit," http://www.nltk.org/.

[17]

N. F. F. da Silva, L. F. Coletta, E. R. Hruschka, and E. R. J. I. S. Hruschka Jr, "Using unsupervised information to improve semi-supervised tweet sentiment classification," vol. 355, pp. 348--365, 2016.

[18]

T. Mikolov, K. Chen, G. Corrado, and J. J. a. p. a. Dean, "Efficient estimation of word representations in vector space," 2013.

[19]

J. Pennington, R. Socher, and C. Manning, "Glove: Global vectors for word representation." pp. 1532--1543.

[20]

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality." pp. 3111--3119.

[21]

M. Kusner, Y. Sun, N. Kolkin, and K. Weinberger, "From word embeddings to document distances." pp. 957--966.

Cited By

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
Show More Cited By

Deep learning for automated sentiment analysis of social media

Recommendations

Deep Learning-Based Sentiment Analysis for Social Media
AIPR '22: Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

Due to the continuous popularization of the Internet and mobile phones, people have gradually entered a participatory network era, and the rapid growth of social networks has caused an explosion of digital information content. It has turned online ...
How Social Media Influencers Govern Sentiment Territory

In present research, the authors examined how social media influencers affect the overall sentiment of a topic. To this end, they utilized supervised machine learning approach to develop SentiRobo for measuring the sentiment score of social media ...
Sentiment Analysis Using Word Polarity of Social Media

Sentiment analysis requires a sentiment dictionary that maps words to sentiments. Further, sentiment weight is an important subtopic in the measurement of the strength of sentiments. A sentiment is the emotional response of an individual toward an ...

Comments

Information & Contributors

Information

Published In

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]

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data

In-Cooperation

IEEE CS

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 January 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Ministry of Science and Technology

Conference

ASONAM '19

Sponsor:

SIGKDD

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

August 27 - 30, 2019

British Columbia, Vancouver, Canada

Acceptance Rates

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

Overall Acceptance Rate 116 of 549 submissions, 21%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

46
Total Citations
View Citations
632
Total Downloads

Downloads (Last 12 months)85
Downloads (Last 6 weeks)6

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

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
Jawad KMahto RDas AAhmed SAziz RKumar P(2023)Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of DepressionApplied Sciences10.3390/app1309532213:9(5322)Online publication date: 24-Apr-2023
https://doi.org/10.3390/app13095322
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents