An Attention-Based Mood Controlling Framework for Social Media Users

Tapotosh Ghosh; Md Hasan Al Banna; Tazkia Mim Angona; Md Jaber Al Nahian; Mohammed Nasir Uddin; M. Shamim Kaiser; Mufti Mahmud

doi:10.1007/978-3-030-86993-9_23

An Attention-Based Mood Controlling Framework for Social Media Users

Tapotosh Ghosh^*, Md Hasan Al Banna, Tazkia Mim Angona, Md Jaber Al Nahian, Mohammed Nasir Uddin, M. Shamim Kaiser, Mufti Mahmud

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

24 Scopus citations

Abstract

In this digital age, social media is an essential part of life. People share their moments and emotions through it. Consequently, detecting emotions in their behavior can be an effective way to determine their emotional disposition, which can then be used to control their negative thinking by making them see the positive aspects of the world. This study proposes an emotion detection-based mood control framework that reorganizes social media posts to match the user’s mental state. An emotion detection model based on Attention mechanism, Bidirectional Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN) has been proposed which can detect six emotions from Bangla text with 66.98% accuracy. It also demonstrates how emotion detection frameworks can be implemented in other languages as well.

Original language	English
Title of host publication	Brain Informatics - 14th International Conference, BI 2021, Proceedings
Editors	Mufti Mahmud, M Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Ning Zhong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	245-256
Number of pages	12
ISBN (Print)	9783030869922
DOIs	https://doi.org/10.1007/978-3-030-86993-9_23
State	Published - 2021
Externally published	Yes
Event	14th International Conference on Brain Informatics, BI 2021 - Virtual, Online Duration: 17 Sep 2021 → 19 Sep 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12960 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Brain Informatics, BI 2021
City	Virtual, Online
Period	17/09/21 → 19/09/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Attention
Detection
Emotion
LSTM
Mood

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-86993-9_23

Cite this

Ghosh, T., Banna, M. H. A., Angona, T. M., Nahian, M. J. A., Uddin, M. N., Kaiser, M. S., & Mahmud, M. (2021). An Attention-Based Mood Controlling Framework for Social Media Users. In M. Mahmud, M. S. Kaiser, S. Vassanelli, Q. Dai, & N. Zhong (Eds.), Brain Informatics - 14th International Conference, BI 2021, Proceedings (pp. 245-256). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12960 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-86993-9_23

Ghosh, Tapotosh ; Banna, Md Hasan Al ; Angona, Tazkia Mim et al. / An Attention-Based Mood Controlling Framework for Social Media Users. Brain Informatics - 14th International Conference, BI 2021, Proceedings. editor / Mufti Mahmud ; M Shamim Kaiser ; Stefano Vassanelli ; Qionghai Dai ; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2021. pp. 245-256 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{8604760c70644205a93988e7d6bef244,

title = "An Attention-Based Mood Controlling Framework for Social Media Users",

abstract = "In this digital age, social media is an essential part of life. People share their moments and emotions through it. Consequently, detecting emotions in their behavior can be an effective way to determine their emotional disposition, which can then be used to control their negative thinking by making them see the positive aspects of the world. This study proposes an emotion detection-based mood control framework that reorganizes social media posts to match the user{\textquoteright}s mental state. An emotion detection model based on Attention mechanism, Bidirectional Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN) has been proposed which can detect six emotions from Bangla text with 66.98\% accuracy. It also demonstrates how emotion detection frameworks can be implemented in other languages as well.",

keywords = "Attention, Detection, Emotion, LSTM, Mood",

author = "Tapotosh Ghosh and Banna, \{Md Hasan Al\} and Angona, \{Tazkia Mim\} and Nahian, \{Md Jaber Al\} and Uddin, \{Mohammed Nasir\} and Kaiser, \{M. Shamim\} and Mufti Mahmud",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 14th International Conference on Brain Informatics, BI 2021 ; Conference date: 17-09-2021 Through 19-09-2021",

year = "2021",

doi = "10.1007/978-3-030-86993-9\_23",

language = "English",

isbn = "9783030869922",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "245--256",

editor = "Mufti Mahmud and Kaiser, \{M Shamim\} and Stefano Vassanelli and Qionghai Dai and Ning Zhong",

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address = "Germany",

}

Ghosh, T, Banna, MHA, Angona, TM, Nahian, MJA, Uddin, MN, Kaiser, MS & Mahmud, M 2021, An Attention-Based Mood Controlling Framework for Social Media Users. in M Mahmud, MS Kaiser, S Vassanelli, Q Dai & N Zhong (eds), Brain Informatics - 14th International Conference, BI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12960 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 245-256, 14th International Conference on Brain Informatics, BI 2021, Virtual, Online, 17/09/21. https://doi.org/10.1007/978-3-030-86993-9_23

An Attention-Based Mood Controlling Framework for Social Media Users. / Ghosh, Tapotosh; Banna, Md Hasan Al; Angona, Tazkia Mim et al.
Brain Informatics - 14th International Conference, BI 2021, Proceedings. ed. / Mufti Mahmud; M Shamim Kaiser; Stefano Vassanelli; Qionghai Dai; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2021. p. 245-256 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12960 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Attention-Based Mood Controlling Framework for Social Media Users

AU - Ghosh, Tapotosh

AU - Banna, Md Hasan Al

AU - Angona, Tazkia Mim

AU - Nahian, Md Jaber Al

AU - Uddin, Mohammed Nasir

AU - Kaiser, M. Shamim

AU - Mahmud, Mufti

PY - 2021

Y1 - 2021

N2 - In this digital age, social media is an essential part of life. People share their moments and emotions through it. Consequently, detecting emotions in their behavior can be an effective way to determine their emotional disposition, which can then be used to control their negative thinking by making them see the positive aspects of the world. This study proposes an emotion detection-based mood control framework that reorganizes social media posts to match the user’s mental state. An emotion detection model based on Attention mechanism, Bidirectional Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN) has been proposed which can detect six emotions from Bangla text with 66.98% accuracy. It also demonstrates how emotion detection frameworks can be implemented in other languages as well.

AB - In this digital age, social media is an essential part of life. People share their moments and emotions through it. Consequently, detecting emotions in their behavior can be an effective way to determine their emotional disposition, which can then be used to control their negative thinking by making them see the positive aspects of the world. This study proposes an emotion detection-based mood control framework that reorganizes social media posts to match the user’s mental state. An emotion detection model based on Attention mechanism, Bidirectional Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN) has been proposed which can detect six emotions from Bangla text with 66.98% accuracy. It also demonstrates how emotion detection frameworks can be implemented in other languages as well.

KW - Attention

KW - Detection

KW - Emotion

KW - LSTM

KW - Mood

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DO - 10.1007/978-3-030-86993-9_23

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AN - SCOPUS:85115864009

SN - 9783030869922

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 245

EP - 256

BT - Brain Informatics - 14th International Conference, BI 2021, Proceedings

A2 - Mahmud, Mufti

A2 - Kaiser, M Shamim

A2 - Vassanelli, Stefano

A2 - Dai, Qionghai

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PB - Springer Science and Business Media Deutschland GmbH

T2 - 14th International Conference on Brain Informatics, BI 2021

Y2 - 17 September 2021 through 19 September 2021

ER -

Ghosh T, Banna MHA, Angona TM, Nahian MJA, Uddin MN, Kaiser MS et al. An Attention-Based Mood Controlling Framework for Social Media Users. In Mahmud M, Kaiser MS, Vassanelli S, Dai Q, Zhong N, editors, Brain Informatics - 14th International Conference, BI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 245-256. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-86993-9_23

An Attention-Based Mood Controlling Framework for Social Media Users

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this