An empirical study on sentiments in Twitter communities

Noha Alduaiji, Amitava Datta

Research output: Chapter in Book/Conference paperConference paper

1 Citation (Scopus)

Abstract

Sentiment analysis and community detection are two popular research subjects in data mining. Lots of research have been published in recent years that aim to enhance the mining of text using sentiment analysis tools and to mine network structure to find cohesive and important communities in social networks. However, there is a lack of knowledge of the importance of understanding the sentiment and its changes on the community lifetime. In this paper, we aim to study the sentiments and its impact on user behaviour and the evolution of social network communities. To do that, we collect three Twitter datasets, two of which are based on the communications between people who share following links and the third dataset is based on people who talked about world cup subject. Next, we analyse the sentiments of communications to positive, negative or neutral. After that, we detect communities using k-core. Later, we track changes of sentiments in communities for an extended period of time. Our results showed that the positive sentiment is contagious because members of the communities increasingly share positive tweets more than the negative ones over time. Also, we found a strong correlation between positive sentiments and the size of the community in all three datasets. These results lay shed on the existence of like-minded users within the communities which attract social network companies for their viral marketing and recommendation systems.

Original languageEnglish
Title of host publicationProceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
EditorsJeffrey Yu, Hanghang Tong, Feida Zhu, Zhenhui Li
Place of PublicationSingapore
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1166-1172
Number of pages7
ISBN (Electronic)9781538692882
DOIs
Publication statusPublished - 7 Feb 2019
Event18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 - Singapore, Singapore
Duration: 17 Nov 201820 Nov 2018

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2018-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
CountrySingapore
CitySingapore
Period17/11/1820/11/18

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Communication
Recommender systems
Data mining
Marketing
Industry

Cite this

Alduaiji, N., & Datta, A. (2019). An empirical study on sentiments in Twitter communities. In J. Yu, H. Tong, F. Zhu, & Z. Li (Eds.), Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 (pp. 1166-1172). [8637428] (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2018-November). Singapore: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDMW.2018.00167
Alduaiji, Noha ; Datta, Amitava. / An empirical study on sentiments in Twitter communities. Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018. editor / Jeffrey Yu ; Hanghang Tong ; Feida Zhu ; Zhenhui Li. Singapore : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 1166-1172 (IEEE International Conference on Data Mining Workshops, ICDMW).
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title = "An empirical study on sentiments in Twitter communities",
abstract = "Sentiment analysis and community detection are two popular research subjects in data mining. Lots of research have been published in recent years that aim to enhance the mining of text using sentiment analysis tools and to mine network structure to find cohesive and important communities in social networks. However, there is a lack of knowledge of the importance of understanding the sentiment and its changes on the community lifetime. In this paper, we aim to study the sentiments and its impact on user behaviour and the evolution of social network communities. To do that, we collect three Twitter datasets, two of which are based on the communications between people who share following links and the third dataset is based on people who talked about world cup subject. Next, we analyse the sentiments of communications to positive, negative or neutral. After that, we detect communities using k-core. Later, we track changes of sentiments in communities for an extended period of time. Our results showed that the positive sentiment is contagious because members of the communities increasingly share positive tweets more than the negative ones over time. Also, we found a strong correlation between positive sentiments and the size of the community in all three datasets. These results lay shed on the existence of like-minded users within the communities which attract social network companies for their viral marketing and recommendation systems.",
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Alduaiji, N & Datta, A 2019, An empirical study on sentiments in Twitter communities. in J Yu, H Tong, F Zhu & Z Li (eds), Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018., 8637428, IEEE International Conference on Data Mining Workshops, ICDMW, vol. 2018-November, IEEE, Institute of Electrical and Electronics Engineers, Singapore, pp. 1166-1172, 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018, Singapore, Singapore, 17/11/18. https://doi.org/10.1109/ICDMW.2018.00167

An empirical study on sentiments in Twitter communities. / Alduaiji, Noha; Datta, Amitava.

Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018. ed. / Jeffrey Yu; Hanghang Tong; Feida Zhu; Zhenhui Li. Singapore : IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 1166-1172 8637428 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2018-November).

Research output: Chapter in Book/Conference paperConference paper

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AB - Sentiment analysis and community detection are two popular research subjects in data mining. Lots of research have been published in recent years that aim to enhance the mining of text using sentiment analysis tools and to mine network structure to find cohesive and important communities in social networks. However, there is a lack of knowledge of the importance of understanding the sentiment and its changes on the community lifetime. In this paper, we aim to study the sentiments and its impact on user behaviour and the evolution of social network communities. To do that, we collect three Twitter datasets, two of which are based on the communications between people who share following links and the third dataset is based on people who talked about world cup subject. Next, we analyse the sentiments of communications to positive, negative or neutral. After that, we detect communities using k-core. Later, we track changes of sentiments in communities for an extended period of time. Our results showed that the positive sentiment is contagious because members of the communities increasingly share positive tweets more than the negative ones over time. Also, we found a strong correlation between positive sentiments and the size of the community in all three datasets. These results lay shed on the existence of like-minded users within the communities which attract social network companies for their viral marketing and recommendation systems.

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Alduaiji N, Datta A. An empirical study on sentiments in Twitter communities. In Yu J, Tong H, Zhu F, Li Z, editors, Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018. Singapore: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 1166-1172. 8637428. (IEEE International Conference on Data Mining Workshops, ICDMW). https://doi.org/10.1109/ICDMW.2018.00167