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Algorithm for prediction of links using sentiment analysis in social networks
P. Sharma, U.K. Singh, T.V. Sharma,
Published in Association for Computing Machinery
Volume: 06-08-July-2016
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. © 2016 ACM.
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