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 language | English |
|---|---|
| Title of host publication | Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 |
| Editors | Jeffrey Yu, Hanghang Tong, Feida Zhu, Zhenhui Li |
| Place of Publication | Singapore |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 1166-1172 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781538692882 |
| DOIs | |
| Publication status | Published - 7 Feb 2019 |
| Event | 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 - Singapore, Singapore Duration: 17 Nov 2018 → 20 Nov 2018 |
Publication series
| Name | IEEE International Conference on Data Mining Workshops, ICDMW |
|---|---|
| Volume | 2018-November |
| ISSN (Print) | 2375-9232 |
| ISSN (Electronic) | 2375-9259 |
Conference
| Conference | 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 17/11/18 → 20/11/18 |
Fingerprint
Cite this
}
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 paper › Conference paper
TY - GEN
T1 - An empirical study on sentiments in Twitter communities
AU - Alduaiji, Noha
AU - Datta, Amitava
PY - 2019/2/7
Y1 - 2019/2/7
N2 - 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.
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.
KW - Like-Minded
KW - Sentiment
KW - Sentiment Evolution
KW - Twitter Communities
UR - http://www.scopus.com/inward/record.url?scp=85062841646&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2018.00167
DO - 10.1109/ICDMW.2018.00167
M3 - Conference paper
T3 - IEEE International Conference on Data Mining Workshops, ICDMW
SP - 1166
EP - 1172
BT - Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
A2 - Yu, Jeffrey
A2 - Tong, Hanghang
A2 - Zhu, Feida
A2 - Li, Zhenhui
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Singapore
ER -