Abstract:
Social Media is one of the generating sources
of big data and analyzing social big data can provide
the valuable information. For analyzing the social
big data in an efficient and timely manner, the
traditional analytic platform is needed to be scaled
up. The powerful technique is necessary to extract the
valuable information from social big data. Sentiment
Analysis can facilitate valuable information by
extracting public opinions. The presence of sarcasm,
an interfering factor that can flip the sentiment of the
given text, is one of the challenges of Sentiment
Analysis. In this paper, Multi-tier Sentiment Analysis
system with sarcasm detection on Hadoop
(MSASDH) is proposed to extract the opinion from
large volumes of tweets. To achieve high-level
performance of sentiment classification, MSASDH
identifies sarcasm and sentiment-emotion by
conducting rule based sarcasm-sentiment detection
scheme and learning based sentiment classification
with Multi-tier architecture. The large amount of
tweets is collected by Apache Flume and it is used for
system evaluation. The evaluation results show that
detecting sarcasm can enhance the accuracy of
Sentiment Analysis. Moreover, the results show that
the MSASDH is efficient and scalable by decreasing
the processing time when adding more nodes into the
cluster.
