Integrated Sentiment and Emotion into Estimating the Similarity Among Entries on Social Network

Nguyen, Thi Hoi; Tran, Dinh Que; Dam, Gia Manh; Nguyễn, Mạnh Hùng

doi:10.1007/978-3-319-74176-5_21

Cited by 2 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In line with our previous works [18,19,20], this paper introduces a model for measuring the similarity between users based on their behavior in social network. In this model, the similarity between users is estimated from the similarity of their behaviors such as posting an entry or sharing an existing entry, liking an entry or liking a comment, commenting on a post, and joining a group or a community.…”

Section: Introductionmentioning

confidence: 77%

A Method to Classify Users on Social Networks Based on Similarity Mesuring

Nguyen

2024

ijmst

View full text Add to dashboard Cite

With the express growth of social networks, users have joined more and more of these networks and live their lives virtually. Consequently, they create huge amounts of data on these social networks: their profile, interests, and behaviors such as posting, commenting, liking, joining groups or communities, etc. One of the basic issues in these challenges is the problem of estimating the similarity among users on these social networks based on their profile, interests, and behavior. This paper presents a model for estimating the similarity between users based ontheir behavior on social networks. The considered behaviors are activities including posting or sharing entries, liking these entries, commenting and liking the comments in these entries, and joining a group in the social networks. The model is then evaluated with a dataset collected from Facebook users. The results show that the model correctly estimates the similarity among users in the majority of cases.

show abstract

Section: Introductionmentioning

confidence: 77%

A Method to Classify Users on Social Networks Based on Similarity Mesuring

Nguyen

2024

ijmst

View full text Add to dashboard Cite

show abstract

“…In line with our previous works ( [13,14]), this paper introduces a model for measuring the similarity between users based on their behavior on social network. In this model, the similarity between users is estimated from the similarity of their behaviors such as posting an entry or sharing an existing entry, liking an entry or liking a comment, commenting on a post, and joining a group or a community.…”

Section: Introductionmentioning

confidence: 78%

“…The similarity of user behavior on these activities is also estimated based on the content of the entries that they post, like, or the content of their comment on these entries from social networks. The similarity among entries is estimated based on the content, tags, category, sentiment, and emotion included in these entries [14]. The model is then evaluated with a dataset-collected users from Twitter.…”

Section: Introductionmentioning

confidence: 99%

Estimating the similarity of social network users based on behaviors

Nguyen

Tran

Dam

et al. 2018

Vietnam J Comput Sci

Self Cite

View full text Add to dashboard Cite

Recently, with the express growth of social network, users have joined more and more of these networks and live their life virtually. Consequently, they create a huge data on these social networks: their profile, interest, and behavior such as post, comment, like, joining groups or communities, etc. This brings some new challenges to researchers: do users having the same profile/interest show the same behavior? And vice versa, do users having the same behavior have interest in the same things? One of the basic issues in these challenges is the problem of estimating the similarity among users on these social networks based on their profile, interest, and behavior. This paper presents a model for estimating the similarity between users based on their behavior on social networks. The considered behaviors are activities including posting entries, liking these entries, commenting and liking the comment in these entries. The model is then evaluated with a dataset-collected users from Twitter. The results show that the model estimates correctly the similarity among users in the majority of the cases.

show abstract

Integrated Sentiment and Emotion into Estimating the Similarity Among Entries on Social Network

Cited by 2 publications

References 10 publications

A Method to Classify Users on Social Networks Based on Similarity Mesuring

A Method to Classify Users on Social Networks Based on Similarity Mesuring

Estimating the similarity of social network users based on behaviors

Contact Info

Product

Resources

About