Algorithm for prediction of negative links using sentiment analysis in social networks
01 Jun 2017-pp 1570-1575
TL;DR: This research paper explains the methodologies that are used to achieve the prediction of negative links between the nodes in the social network.
Abstract: The social network being one of the most disruptive innovations of the last decade has gathered a huge amount of attention of the people. The posts of the users of 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 the social network. One of the aspect that we were able to discover was about finding the relationship between the users (i.e., 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 the negative link can be applied in the 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 doing things together even though there is no relation between them. It can also be used in improving the recommendation system in social media as if there is some probability between the two nodes of being the 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 sentiment analysis, which divides the users into five simple categories: Extremely +ve(i.e.,positive), +ve, Neutral, -ve (i.e.,negative) and Extremely -ve. This research paper explains the methodologies that we have used to achieve the prediction of negative links between the nodes in the social network.
Citations
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TL;DR: A novel Hierarchical Deep Fusion (HDF) model is proposed to explore the cross-modal correlations among images, texts, and their social links, which can learn comprehensive and complementary features for more effective sentiment analysis.
37 citations
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11 Jan 2018TL;DR: In this article, the authors focused on the educational posts gathered from the Twitter social media and used web analytics technique to gather and analyze insights into community actions and attitudes through data collection, pre-processing, classification, and analysis of results.
Abstract: Social Networking on social media websites involves the use of the internet to connect users with their friends, family and acquaintances. Due to the increasing influence of social media such as Twitter, more number of users participates in the discussion and different users belong to different kind of groups. Positive, negative, and neutral comments are posted by the user and they participate in the discussion. The study mainly focused on the educational posts gathered from the Twitter social media. The web analytics technique was used in gathering and analyzing insights into community actions and attitudes through data collection, pre-processing, classification, and analysis of results. The collected tweets from February 1, 2017 to March 30, 2017 were 1,717 using the keywords Philippine education, DepEd K-12 and CHED K-12. After cleaning, 1,548 tweets were derived and classified as positive, negative, and neutral with 74.9% accuracy evaluation level. Results showed that mostly had expressed their negativity on the implementation of the K-12 program in the country. Measures such as hiring of teachers, sufficient allocation and utilization of funds for the procurement of books and resources, and sending the teachers concerned for training is deemed necessary to address the sentiments of the people. It is recommended then that an in-depth study of the K-12 implementation may be conducted to further improve the Philippine educational system. Results may also be presented to the officials of DepEd for validation and consideration to further enhance the implementation of K-12 program, as the next step of the study.
9 citations
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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.
Abstract: Signed network analysis being one of the greatest disruptive innovations within the last decade has assembled a vast amount of attention of the citizenry. The positions of the users of the signed networks are used by several societies in the world to see the mentality of the users, the current movement of the grocery store and many more things. But even so, in that location is a latent potential of social nets. Ace of the facial expressions that, we were able to determine was about seeing the relationship between the users (i.e., especially, the negative (i.e., −Ve) link in social networks) on the signed network using the stakes that the users work and the reaction of the other users towards it. The anticipation of a negative link (i.e., −Ve) can be applied in the information security field, to observe the aberrations in the largest social networks and further discover the malicious nodes in the larger social network; say, if two nodes are doing things together even though in that respect is no intercourse between them. It can likewise be utilized in improving the recommendation system in social networks as if there is some probability between the two the nodes of being an enemy or disliking each other then we can slay them from each other’s recommendation list or could assign a lesser weight to them in a recommended technique. To accomplish all this relationship between the nodes we first need to determine whether the user is posting posts with positive emotion (like happy, excited, etc.) or negative emotion (like angry, sad, and so on), and then that we can further examine the learning ability of the user and utilize it to recommend the people who we have previously separated with the similar personality. For that we have applied the sentiment analysis in social networks, which splits up the users into five simple categories: Highly Positive (i.e., Highly +Ve), Positive (i.e., +Ve), Neutral, Negative (i.e., −Ve) and Highly Negative (i.e., Highly −Ve).
6 citations
References
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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.
Abstract: Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network. 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. © 2007 Wiley Periodicals, Inc.
4,181 citations
"Algorithm for prediction of negativ..." refers background in this paper
...It is believed that predicting negative links essentially improves positive link prediction, and it can prove to be very useful in recommendation systems [16] in social media....
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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.
Abstract: We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We 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. 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; they also 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.
1,253 citations
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17 Mar 2011TL;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.
Abstract: Link prediction is an important task for analying social networks which also has applications in other domains like, information retrieval, bioinformatics and e-commerce There exist a variety of techniques for link prediction, ranging from feature-based classification and kernel-based method to matrix factorization and probabilistic graphical models These methods differ from each other with respect to model complexity, prediction performance, scalability, and generalization ability In this article, we survey some representative link prediction methods by categorizing them by the type of the models We largely consider three types of models: first, the traditional (non-Bayesian) models which extract a set of features to train a binary classification model Second, the probabilistic approaches which model the joint-probability among the entities in a network by Bayesian graphical models And, finally the linear algebraic approach which computes the similarity between the nodes in a network by rank-reduced similarity matrices We discuss various existing link prediction models that fall in these broad categories and analyze their strength and weakness We conclude the survey with a discussion on recent developments and future research direction
566 citations
"Algorithm for prediction of negativ..." refers methods in this paper
...different methods to extract the hidden negative interactions out of the available +ve links and the information-centric interactions between users, & predicts a future network [17], [18] with some new links that might exist at a point of time in future....
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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.
Abstract: Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple disciplines. With the increasing prevalence of social media networks, signed network analysis has evolved from developing and measuring theories to mining tasks. In this article, we present a review of mining signed networks in the context of social media and discuss some promising research directions and new frontiers. We begin by giving basic concepts and unique properties and principles of signed networks. Then we classify and review tasks of signed network mining with representative algorithms. We also delineate some tasks that have not been extensively studied with formal definitions and also propose research directions to expand the field of signed network mining.
210 citations
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01 Jan 2016TL;DR: This chapter explores applications of sentiment analysis and demonstrates how sentiment mining in social media can be exploited to determine how local crowds react during a disaster, and how such information can be used to improve disaster management.
Abstract: Sentiment analysis refers to the class of computational and natural language processing based techniques used to identify, extract or characterize subjective information, such as opinions, expressed in a given piece of text. The main purpose of sentiment analysis is to classify a writer’s attitude towards various topics into positive, negative or neutral categories. Sentiment analysis has many applications in different domains including, but not limited to, business intelligence, politics, sociology, etc. Recent years, on the other hand, have witnessed the advent of social networking websites, microblogs, wikis and Web applications and consequently, an unprecedented growth in user-generated data is poised for sentiment mining. Data such as web-postings, Tweets, videos, etc., all express opinions on various topics and events, offer immense opportunities to study and analyze human opinions and sentiment. In this chapter, we study the information published by individuals in social media in cases of natural disasters and emergencies and investigate if such information could be used by first responders to improve situational awareness and crisis management. In particular, we explore applications of sentiment analysis and demonstrate how sentiment mining in social media can be exploited to determine how local crowds react during a disaster, and how such information can be used to improve disaster management. Such information can also be used to help assess the extent of the devastation and find people who are in specific need during an emergency situation. We first provide the formal definition of sentiment analysis in social media and cover traditional and the state-of-the-art approaches while highlighting contributions, shortcomings, and pitfalls due to the composition of online media streams. Next we discuss the relationship among social media, disaster relief and situational awareness and explain how social media is used in these contexts with the focus on sentiment analysis. In order to enable quick analysis of real-time geo-distributed data, we will detail applications of visual analytics with an emphasis on sentiment visualization . Finally, we conclude the chapter with a discussion of research challenges in sentiment analysis and its application in disaster relief.
165 citations
"Algorithm for prediction of negativ..." refers background in this paper
..., Extremely +ve/+ve/Neutral/-ve/Highly -ve) [6], [7], [8] to the links that exist between the social network users, representing the level of friendship or trust [6] between them....
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