Polarity Switch within Social Networks

Abstract

It is the age of information. Social networks are the main reason why, following the increasing activity of online users. With this comes a big impact on the real world, it can be positive and highly negative as well. Therefore, research in this field is highly needed for the betterment of societal behaviors within social networks, to minimize the negative impact. When a negatively polarized message spreads over time, it is eventually exposed to most users of that social network where it is spreading. Social network communities start behaving in a negatively polarized way, due to the negative nature of the propagation of information. In order to optimize technological intervention to the benefit of humanity, in this paper an approach is elaborated in order to help switch the dominant polarity in the case of negativity. The platform of reference is Twitter, since it classifies trends per area, which polarities' dominate within the social network. To achieve that, an extraction as well as a sentiment analysis method are developed to initiate the positive polarity introduction in the network. The idea behind introducing a positive polarity in a network, relies on the studies of the positive effect nature has on users. The positively polarized promoted content is selected from sponsored content of users who consent to spreading their posts.

Detecting Topics and Polarity From Twitter: A University Faculty Case

“…Ideally, the aim is to maximize the positive influence of the entities in the networks while minimizing negative comments. To this end, Polarity-related Independent Cascade (IC-P) diffusion model [11], techniques based on emotional calm states in which disruptive users are added as potential customers for the entities [12], are proposed. The characteristics found in the texts and the social network features: URLs, hashtags, and emoticons, among others, deserve a detailed analysis to discover texts' polarity [13].…”

Detecting topics and polarity from Twitter: the ETSII-UPM case

“…Ideally, the aim is to maximize the positive influence of the entities in the networks while minimizing negative comments. To this end, Polarity-related Independent Cascade (IC-P) diffusion model (Li et al, 2014), techniques based on emotional calm states in which disruptive users are added as potential customers for the entities (Abas, Addou, & Rachik, 2020), are proposed. The characteristics found in the texts and the social network features: URLs, hashtags and emoticons, among others, deserve a particular analysis to discover texts' polarity (Varga, Lezama, & Payares, 2021).…”

Prediction of customer’s perception in social networks by integrating sentiment analysis and machine learning

“…In the case of reaching a big audience, their angry content spreads all over the social platform. As an attempt to identify such bad effect among customers who share conversations within the same social platform, this paper proposes a framework to detect such a polarity switch (Abas et al, 2020) in customer conversation with customer service provider. Polarity switch is concerned with switching the user's mood from a positive optimistic one to negative one (or vice versa).…”