A Sentimental and Semantical Analysis on Facebook Comments to Detect Latent Patterns

Mahdi Naser Moghadasi; Zohreh Safari; Yu Zhuang

doi:10.1109/BigData50022.2020.9378425

A Sentimental and Semantical Analysis on Facebook Comments to Detect Latent Patterns

Mahdi Naser Moghadasi, Zohreh Safari, Yu Zhuang

Computer Science

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

5 Scopus citations

Abstract

Social media posts and their comments are rich in variation of subjects and an interesting pool for opinion mining. Individuals engage in social communications on Facebook through three behaviors: like, share and comment on a Facebook post. Responses to comments are grouped under the respective comment as a conversation thread. Conversation threads become interesting when users have conflicting views with the article posted, or with the opinion of another user. Our research goal is to answer questions such as why some posts in Facebook receive more attention than others? Are conversation threads following a similar pattern between subjects like sport and politics? Is there any harmony between conversation threads of different subjects? We investigated how individuals react to different conversation subjects in the Facebook through a comprehensive analysis. Our aim is to discover semantic and sentimental patterns in conversation threads categories. Finally, we employed Natural Language Processing techniques such as semantic and sentimental analysis and statistical methods like average response time (ART) and average comment length (ACL) of a post and observed that there are interesting patterns exists among different conversation threads.

Original language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
Editors	Xintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4665-4671
Number of pages	7
ISBN (Electronic)	9781728162515
DOIs	https://doi.org/10.1109/BigData50022.2020.9378425
State	Published - Dec 10 2020
Event	8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States Duration: Dec 10 2020 → Dec 13 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference	8th IEEE International Conference on Big Data, Big Data 2020
Country/Territory	United States
City	Virtual, Atlanta
Period	12/10/20 → 12/13/20

Keywords

Data Mining
Semantic Analysis
Social Analysis

Access to Document

10.1109/BigData50022.2020.9378425

Cite this

Moghadasi, M. N., Safari, Z., & Zhuang, Y. (2020). A Sentimental and Semantical Analysis on Facebook Comments to Detect Latent Patterns. In X. Wu, C. Jermaine, L. Xiong, X. T. Hu, O. Kotevska, S. Lu, W. Xu, S. Aluru, C. Zhai, E. Al-Masri, Z. Chen, & J. Saltz (Eds.), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp. 4665-4671). [9378425] (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData50022.2020.9378425

Moghadasi, Mahdi Naser ; Safari, Zohreh ; Zhuang, Yu. / A Sentimental and Semantical Analysis on Facebook Comments to Detect Latent Patterns. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. editor / Xintao Wu ; Chris Jermaine ; Li Xiong ; Xiaohua Tony Hu ; Olivera Kotevska ; Siyuan Lu ; Weijia Xu ; Srinivas Aluru ; Chengxiang Zhai ; Eyhab Al-Masri ; Zhiyuan Chen ; Jeff Saltz. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 4665-4671 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

@inproceedings{e9229fbe7329475f800c51a4a238ff5d,

title = "A Sentimental and Semantical Analysis on Facebook Comments to Detect Latent Patterns",

abstract = "Social media posts and their comments are rich in variation of subjects and an interesting pool for opinion mining. Individuals engage in social communications on Facebook through three behaviors: like, share and comment on a Facebook post. Responses to comments are grouped under the respective comment as a conversation thread. Conversation threads become interesting when users have conflicting views with the article posted, or with the opinion of another user. Our research goal is to answer questions such as why some posts in Facebook receive more attention than others? Are conversation threads following a similar pattern between subjects like sport and politics? Is there any harmony between conversation threads of different subjects? We investigated how individuals react to different conversation subjects in the Facebook through a comprehensive analysis. Our aim is to discover semantic and sentimental patterns in conversation threads categories. Finally, we employed Natural Language Processing techniques such as semantic and sentimental analysis and statistical methods like average response time (ART) and average comment length (ACL) of a post and observed that there are interesting patterns exists among different conversation threads.",

keywords = "Data Mining, Semantic Analysis, Social Analysis",

author = "Moghadasi, {Mahdi Naser} and Zohreh Safari and Yu Zhuang",

note = "Funding Information: The work reported in this paper was supported in part by National Science Foundation under Grant No. CNS-1526055. Publisher Copyright: {\textcopyright} 2020 IEEE.; null ; Conference date: 10-12-2020 Through 13-12-2020",

year = "2020",

month = dec,

day = "10",

doi = "10.1109/BigData50022.2020.9378425",

language = "English",

series = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4665--4671",

editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, {Xiaohua Tony} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",

booktitle = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",

}

Moghadasi, MN, Safari, Z & Zhuang, Y 2020, A Sentimental and Semantical Analysis on Facebook Comments to Detect Latent Patterns. in X Wu, C Jermaine, L Xiong, XT Hu, O Kotevska, S Lu, W Xu, S Aluru, C Zhai, E Al-Masri, Z Chen & J Saltz (eds), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020., 9378425, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, Institute of Electrical and Electronics Engineers Inc., pp. 4665-4671, 8th IEEE International Conference on Big Data, Big Data 2020, Virtual, Atlanta, United States, 12/10/20. https://doi.org/10.1109/BigData50022.2020.9378425

A Sentimental and Semantical Analysis on Facebook Comments to Detect Latent Patterns. / Moghadasi, Mahdi Naser; Safari, Zohreh; Zhuang, Yu.

Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. ed. / Xintao Wu; Chris Jermaine; Li Xiong; Xiaohua Tony Hu; Olivera Kotevska; Siyuan Lu; Weijia Xu; Srinivas Aluru; Chengxiang Zhai; Eyhab Al-Masri; Zhiyuan Chen; Jeff Saltz. Institute of Electrical and Electronics Engineers Inc., 2020. p. 4665-4671 9378425 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

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

TY - GEN

T1 - A Sentimental and Semantical Analysis on Facebook Comments to Detect Latent Patterns

AU - Moghadasi, Mahdi Naser

AU - Safari, Zohreh

AU - Zhuang, Yu

PY - 2020/12/10

Y1 - 2020/12/10

N2 - Social media posts and their comments are rich in variation of subjects and an interesting pool for opinion mining. Individuals engage in social communications on Facebook through three behaviors: like, share and comment on a Facebook post. Responses to comments are grouped under the respective comment as a conversation thread. Conversation threads become interesting when users have conflicting views with the article posted, or with the opinion of another user. Our research goal is to answer questions such as why some posts in Facebook receive more attention than others? Are conversation threads following a similar pattern between subjects like sport and politics? Is there any harmony between conversation threads of different subjects? We investigated how individuals react to different conversation subjects in the Facebook through a comprehensive analysis. Our aim is to discover semantic and sentimental patterns in conversation threads categories. Finally, we employed Natural Language Processing techniques such as semantic and sentimental analysis and statistical methods like average response time (ART) and average comment length (ACL) of a post and observed that there are interesting patterns exists among different conversation threads.

AB - Social media posts and their comments are rich in variation of subjects and an interesting pool for opinion mining. Individuals engage in social communications on Facebook through three behaviors: like, share and comment on a Facebook post. Responses to comments are grouped under the respective comment as a conversation thread. Conversation threads become interesting when users have conflicting views with the article posted, or with the opinion of another user. Our research goal is to answer questions such as why some posts in Facebook receive more attention than others? Are conversation threads following a similar pattern between subjects like sport and politics? Is there any harmony between conversation threads of different subjects? We investigated how individuals react to different conversation subjects in the Facebook through a comprehensive analysis. Our aim is to discover semantic and sentimental patterns in conversation threads categories. Finally, we employed Natural Language Processing techniques such as semantic and sentimental analysis and statistical methods like average response time (ART) and average comment length (ACL) of a post and observed that there are interesting patterns exists among different conversation threads.

KW - Data Mining

KW - Semantic Analysis

KW - Social Analysis

UR - http://www.scopus.com/inward/record.url?scp=85103818920&partnerID=8YFLogxK

U2 - 10.1109/BigData50022.2020.9378425

DO - 10.1109/BigData50022.2020.9378425

M3 - Conference contribution

AN - SCOPUS:85103818920

T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

SP - 4665

EP - 4671

BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

A2 - Wu, Xintao

A2 - Jermaine, Chris

A2 - Xiong, Li

A2 - Hu, Xiaohua Tony

A2 - Kotevska, Olivera

A2 - Lu, Siyuan

A2 - Xu, Weijia

A2 - Aluru, Srinivas

A2 - Zhai, Chengxiang

A2 - Al-Masri, Eyhab

A2 - Chen, Zhiyuan

A2 - Saltz, Jeff

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 10 December 2020 through 13 December 2020

ER -

Moghadasi MN, Safari Z, Zhuang Y. A Sentimental and Semantical Analysis on Facebook Comments to Detect Latent Patterns. In Wu X, Jermaine C, Xiong L, Hu XT, Kotevska O, Lu S, Xu W, Aluru S, Zhai C, Al-Masri E, Chen Z, Saltz J, editors, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 4665-4671. 9378425. (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). doi: 10.1109/BigData50022.2020.9378425

A Sentimental and Semantical Analysis on Facebook Comments to Detect Latent Patterns

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