  In the past few years, there has been a huge growth in Twitter sentiment
analysis having already provided a fair amount of research on sentiment
detection of public opinion among Twitter users. Given the fact that Twitter
messages are generated constantly with dizzying rates, a huge volume of
streaming data is created, thus there is an imperative need for accurate
methods for knowledge discovery and mining of this information. Although there
exists a plethora of twitter sentiment analysis methods in the recent
literature, the researchers have shifted to real-time sentiment identification
on twitter streaming data, as expected. A major challenge is to deal with the
Big Data challenges arising in Twitter streaming applications concerning both
Volume and Velocity. Under this perspective, in this paper, a methodological
approach based on open source tools is provided for real-time detection of
changes in sentiment that is ultra efficient with respect to both memory
consumption and computational cost. This is achieved by iteratively collecting
tweets in real time and discarding them immediately after their process. For
this purpose, we employ the Lexicon approach for sentiment characterizations,
while change detection is achieved through appropriate control charts that do
not require historical information. We believe that the proposed methodology
provides the trigger for a potential large-scale monitoring of threads in an
attempt to discover fake news spread or propaganda efforts in their early
stages. Our experimental real-time analysis based on a recent hashtag provides
evidence that the proposed approach can detect meaningful sentiment changes
across a hashtags lifetime.
