Paper Info
Title
Little Bad Concerns: Using Sentiment Analysis to Assess Structural Balance in Communication Networks.
Abstract
We present and test a scalable approach for assigning valence to links in unsigned graphs with the ultimate goal of enabling triadic balanced assessment in communication networks. We do this by applying domain-adjusted sentiment analysis to the content of communication data and translating aggregated sentiment scores for information exchanged between network members into link signs. This approach facilitates fast, informed and systematic balance testing (we generate link signs for 166,670 triads in our data); allowing for empirical hypothesis testing and theory building based on current or archival communication data. The proposed technique eliminates the need for manually labeling text data, and overcomes limitations with inferring valence from self-reported or user-generated (meta-) data in situations where historical context and ground truth valence data might be unavailable or limited. We test this approach on corporate email data to complement the large amount of prior work based on social media data and the limited knowledge on sentiment in professional settings. Our results suggest that sentiment is overall slightly positive and emotionality is low, which reflects conventions of language use in a corporate environment. We observe that people draw from (the top of) a smaller pool of positive terms more frequently than from a larger set of negative terms. The ratio of balanced triads (on average about 88%) to unbalanced triads (12%) remains relatively stable despite changes in corporate performance. The labor-intense adjustment of a given lexical resource to some dataset and domain pays off as it generates more empirical evidence with lower variance.
Year
DOI
Venue
2015
10.1145/2808797.2809403
ASONAM '15: Advances in Social Networks Analysis and Mining 2015 Paris France August, 2015
Keywords
Field
DocType
Structural balance theory, signed graphs, text mining, sentiment analysis
Data mining,Text mining,Social media,Telecommunications network,Empirical evidence,Computer science,Sentiment analysis,Ground truth,Artificial intelligence,Machine learning,Statistical hypothesis testing,Scalability
Conference
ISBN
Citations 
PageRank 
978-1-4503-3854-7
3
0.38
References 
Authors
11
2
Name
Order
Citations
PageRank
Jana Diesner121624.38
Craig S. Evans230.38