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Blogvox2: A Modular Domain Independent Sentiment Analysis SystemTweetSpeaker: Sandeep Balijepalli Start: Thursday, June 07, 2007, 01:00PM End: Thursday, June 07, 2007, 03:00PM Location: 325b ITE Abstract: Bloggers make a huge impact on society by representing and
Influencing the people. Blogging by nature is about expressing
and listening to opinion, the job of a politician is to both
represent and lead the people, good sentiment detection tools,
for blogs and other social media, tailored to politics are a must
for today's society. With the elections around the corner,
political blogs are vital to exerting and keeping political
influence over society. Currently, no sentiment analysis
framework that is tailored to Political Blogs exists. Hence, a
modular framework built with replicable modules for the analysis
of sentiment in blogs tailored to political blogs is thus
justified.
In this paper, I propose Blogvox2: an information retrieval based
modular domain independent sentiment analysis framework that uses
customized pattern matching techniques, nave Bayesian filter, bag
of words and part of speech tagging techniques for opinion
extraction in blogs. We also developed prototype two-panel and
four-panel search applications of the query results. Also, trends
on the hot and top topics on the opinionated sentences are
analyzed.
By this framework, the benefits of Blogvox2 we created a modular
approach provides a platform where new modules for different
domains can be easily plugged in. The framework provides the date
of publishing, permanent link and the URLs of the sentences that
expresses opinions based on the analysis. Additionally, tools for
trend analysis for obtaining the hot and top topic identification
graphics based on the obtained opinionated sentences for
presented.
Based on the analysis of the blogvox2 on political domain, our
system performs well with Unigram approach. We investigated our
framework with pattern matching techniques, bigram techniques, and
incorporating parts of speech tagging, which haven't fared as
well as unigram techniques, although combining the unigram and
bigram techniques performed similar to the unigram approach. We
also investigated the reasons for the performance degradation or
enhancements on each approach. Based on our analysis, we also
developed different applications the ease of using the framework. Tags: blog, sentiment analysis system, opinions, trend analysis , |