One months has passed by, and we have all been more familiar with our courses, our teachers, and certainly our classmates. through the discussion with each other, some basic conceptions have been more explicit. And just see what we’ve learned these days.
Sentiment Analysis and Opinion Mining
the pic above showed a general structure of sentiment analysis system in some certain of elearning website overseas.from the pic,we can tell the goals is Enabling subjectivity and sentiment analysis for generating feedback from user generated discourse and for supporting information search in this eLearning website
- investigate knowledge- and corpus-based methods for subjectivity and sentiment analysis
- determine the semantic orientation and strength of the opinions
- identify the targets of the opinions
- identify the holders of the opinions
the vertical dimension showed the score of SO(subjective and objective)popularity, while the horizon axe showed the score of PN(positive and negative)popularity, what may be confused is that there is much more wide range of subjective words when comparing to objective words, and it's all got well explanation during our class. however the dictionary of sentiwordnet cannot cover all the words, and this noticeable problem is yet to be addressed by other methods.


Analyzing other people's minds makes it easier to have better understandings about them.
回复删除Analysis of Twitter consists of searching the tweets on a topic and analyzing their text. Many tweets do not have enough textual content – they may just contain a URL pointing somewhere and a couple of words. So the engine needs to extract a decent volume of tweets and parse them for sentiment expressions, and for key concepts that are being discussed (semantics).
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