Opinion Mining and Social Networks:
a Promising Match
Abstract—In this paper we discuss the role and importance of social networks as preferred environments for opinion mining and sentiment analysis especially. We begin by briefly describing selected properties of social networks that are relevant with respect to opinion mining and we outline the general relationships between the two disciplines. We present the related work and provide basic definitions used in opinion mining. Then, we introduce our original method of opinion classification and we test the presented algorithm on real world datasets acquired from popular Polish social networks, reporting on the results. The results are promising and soundly support the main thesis of the paper, namely, that social networks exhibit properties that make them very suitable for opinion mining activities.
Keywords: opinion mining, sentiment analysis, social
computing, social networks
I. INTRODUCTION
Graphs and networks certainly rank among one of the most popular data representation models due to their universal applicability to various application domains. The need to analyze and mine interesting knowledge from graph and network structures has been long recognized, but only recently the advances in information systems have enabled the analysis of graph structures at huge scales. Analysis of graph and network structures gained new momentum with the advent of social networks. While the analysis of social networks has been a field of intensive research, particularly in the domains of social sciences and psychology, economy or chemistry, it is the emergence of huge social networking services over the Web that
spawned the research into large-scale structural properties of social networks.. Social networks exhibit a very clear community structure. Such community structure partially stems from objective limitations (e.g., internal organizational structure of a company can be closely represented by the ties within a particular social network) or, to some extent, may result from subjective user actions and activities (e.g., bonding with other people who share one’s interests and hobbies). Unveiling the true structure of a social network and understanding of communities forming within the network is the key factor in understanding what the future structure of network will be. The main goal of social network analysis is the study of structural properties of networks. Structural analysis of the social network investigates the properties of individual vertices and the global properties of the network as a whole. It answers two basic classes of questions about the network: what is the structural position of any given individual node and what can be said about groups (communities) forming within the network. The main measurement of a node’s social power (also called member’s prestige) is centrality, which allows to determine node’s relative and absolute importance in the network. There are several methods to determine node’s centrality, such as the degree centrality (the number of links that connect to a given node), the betweenness centrality (the number of shortest paths between any pair of nodes in the network that traverse a given node) or the closeness centrality (the mean of shortest paths lengths to other nodes in the network). From the point of view of opinion mining the ability to assess the node’s prestige is essential as it allows to differentiate between opinions of different individuals. More specifically, node’s prestige allows to assign different weights to opinions and associate more importance to opinions expressed by prominent individuals. Another factor that is often considered in opinion mining is the identification of influential individuals. An influential individual does not have to be necessarily characterized with high degree centrality to influence the average opinion within the network. Usually, such individuals are characterized by high betweenness
centrality, impacting the dissemination of opinion rather than forming the opinion. For instance, an individual with high betweenness centrality can stop a negative opinion from spreading through the network, or, on the other hand, she can amplify the opinion. Due to psychological reasons humans tend to form their opinions in such way that the opinions conform with the norm established within a given social group. Thus,
when mining opinions one has to take into consideration the influence of the context in which the opinion is forming, i.e. the social milieu of an individual. Social networks are highly effective in bolstering group formation
