143,160 research outputs found
Determination of Friendship Intensity between Online Social Network Users Based on Their Interaction
Online social networks (OSN) are one of the most popular forms of modern
communication and among the best known is Facebook. Information about the
connection between users on the OSN is often very scarce. It's only known if
users are connected, while the intensity of the connection is unknown. The aim
of the research described was to determine and quantify friendship intensity
between OSN users based on analysis of their interaction. We built a
mathematical model, which uses: supervised machine learning algorithm Random
Forest, experimentally determined importance of communication parameters and
coefficients for every interaction parameter based on answers of research
conducted through a survey. Taking user opinion into consideration while
designing a model for calculation of friendship intensity is a novel approach
in opposition to previous researches from literature. Accuracy of the proposed
model was verified on the example of determining a better friend in the offered
pair
Exploiting Social Network Structure for Person-to-Person Sentiment Analysis
Person-to-person evaluations are prevalent in all kinds of discourse and
important for establishing reputations, building social bonds, and shaping
public opinion. Such evaluations can be analyzed separately using signed social
networks and textual sentiment analysis, but this misses the rich interactions
between language and social context. To capture such interactions, we develop a
model that predicts individual A's opinion of individual B by synthesizing
information from the signed social network in which A and B are embedded with
sentiment analysis of the evaluative texts relating A to B. We prove that this
problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss
Markov random field, and we show that this implementation outperforms text-only
and network-only versions in two very different datasets involving
community-level decision-making: the Wikipedia Requests for Adminship corpus
and the Convote U.S. Congressional speech corpus
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