TITLE:
Enhancing Sentiment Analysis on Twitter Using Community Detection
AUTHORS:
William Deitrick, Benjamin Valyou, Wes Jones, Joshua Timian, Wei Hu
KEYWORDS:
Community Detection; Twitter; Social Networks; Sentiment Analysis; SentiWordNet; Walktrap
JOURNAL NAME:
Communications and Network,
Vol.5 No.3,
August
2,
2013
ABSTRACT: The increasing popularity of social media
in recent years has created new opportunities to study the interactions of different
groups of people. Never before have so many data about such a large number of
individuals been readily available for analysis. Two popular topics in the
study of social networks are community detection and sentiment analysis.
Community detection seeks to find groups of associated individuals within
networks, and sentiment analysis attempts to determine how individuals are
feeling. While these are generally treated as separate issues, this study takes
an integrative approach and uses community detection output to enable
community-level sentiment analysis. Community detection is performed using the
Walktrap algorithm on a network of Twitter users associated with Microsoft
Corporation’s @technet account. This
Twitter account is one of several used by Microsoft Corporation primarily for
communicating with information technology professionals. Once community
detection is finished, sentiment in the tweets produced by each of the
communities detected in this network is analyzed based on word sentiment scores
from the well-known SentiWordNet lexicon. The combination of sentiment analysis
with community detection permits multilevel exploration of sentiment
information within the @technet network, and demonstrates the power of combining these two techniques.