Proceedings ArticleDOI
Algorithm for Prediction of Links using Sentiment Analysis in Social Networks
Pushkar Sharma,Usha Kiran Singh,T. Virajita Sharma,Debasis Das +3 more
- pp 29
TLDR
This research paper explains the methodologies used to achieve the prediction of negative links between the nodes in the social network using the sentimental analysis which divides the users into five simple categories: Highly Positive, Positive, Neutral, Negative and Highly Negative.Abstract:
Social network being one of the most disruptive innovation has gathered a huge amount of attention of the people within the last decade. The posts of the users on the social media are used by many companies in the world to find the mentality of the users, the current trend of the market and many more things. But still there is a latent potential in social network. One of the aspect that we were able to discover was of finding the relationship between the users (especially, the negative link) on the social network using the posts that the users make and the reaction of the other users towards it. The prediction of negative link can be applied in cyber-security field, to observe the aberrations in the network and further find the malicious nodes in the social network; say, if two nodes are are having a link between them even though there is no relation between them. It can also be used for improving the recommendation system in social media, as if there is some probability between the two nodes of being enemy or disliking each other then we can remove them from each other's recommendation list or could assign a lower weight to them in our recommendation algorithm. To achieve all this relationship between the nodes we first need to find whether the user is posting posts with positive emotion (like happy, excited, etc.) or negative emotion (like angry, sad, etc.) so that we can further analyze the mentality of the user and use it to recommend the people who we have previously classified with the similar personality. For that we have used the sentimental analysis which divides the users into five simple categories: Highly Positive, Positive, Neutral, Negative and Highly Negative. This research paper explains the methodologies that we have used to achieve the prediction of negative links between the nodes in the social network.read more
Citations
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Journal ArticleDOI
Over a Decade of Social Opinion Mining: A Systematic Review
Keith Cortis,Brian Davis +1 more
TL;DR: Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple media formats, like text, image, video and audio.
Posted Content
Over a Decade of Social Opinion Mining.
Keith Cortis,Brian Davis +1 more
TL;DR: A thorough systematic review was carried out on Social Opinion Mining research, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats.
Journal ArticleDOI
Positive and Negative Link Prediction Algorithm Based on Sentiment Analysis in Large Social Networks
TL;DR: Ace of the facial expressions that, was able to determine was about seeing the relationship between the users on the signed network using the stakes that the users work and the reaction of the other users towards it, and applied the sentiment analysis in social networks.
Journal ArticleDOI
Context Aware Sentiment Link Prediction in Heterogeneous Social Network
Anping Zhao,Yu Yu +1 more
TL;DR: The proposed heterogeneous social network embedding-based approach is effective and feasible for detecting unobserved sentiment links from online social networks and outperforms the state-of-the-art baselines in sentiment link prediction tasks.
Journal ArticleDOI
Over a decade of social opinion mining: a systematic review
Keith Cortis,Brian Davis +1 more
TL;DR: Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple media formats, like text, image, video and audio as discussed by the authors.
References
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Journal IssueDOI
The link-prediction problem for social networks
David Liben-Nowell,Jon Kleinberg +1 more
TL;DR: Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.
Posted Content
Predicting Positive and Negative Links in Online Social Networks
TL;DR: In this article, the authors study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (ending up with opposition or antagonism) and find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites.
Proceedings ArticleDOI
Predicting positive and negative links in online social networks
TL;DR: These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology and suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.
Book ChapterDOI
A Survey of Link Prediction in Social Networks
TL;DR: This article surveys some representative link prediction methods by categorizing them by the type of models, largely considering three types of models: first, the traditional (non-Bayesian) models which extract a set of features to train a binary classification model, and second, the probabilistic approaches which model the joint-probability among the entities in a network by Bayesian graphical models.
Journal ArticleDOI
A Survey of Signed Network Mining in Social Media
TL;DR: A review of mining signed networks in the context of social media and discuss some promising research directions and new frontiers can be found in this article, where the authors classify and review tasks of signed network mining with representative algorithms.