Rephrase this text <In both the subjectivity classifier NAME_1 polarity classifier, the same targetindependent feature set is used.
In (NAME_2 NAME_3, 2004) , opinions are extracted from product reviews, where the features of the product are considered opinion targets.
Besides the above mentioned work for targetindependent sentiment classification, there are also several approaches proposed for target-dependent classification, such as (NAME_4, 2003; NAME_2 NAME_3, 2004; NAME_5 NAME_3, 2007) . (NAME_4, 2003) adopt a rule based approach, where rules are created by humans for adjectives, verbs, nouns, NAME_1 so on.
As Twitter becomes more popular, sentiment analysis on Twitter data becomes more attractive. (NAME_6 NAME_7 NAME_8., 2009; NAME_9 NAME_1 Movassate, 2009; NAME_10 NAME_1 NAME_11, 2010; NAME_12 NAME_7 NAME_8., 2010) all follow the machine learning based approach for sentiment classification of tweets.
In our approach, rich feature representations are used to distinguish between sentiments expressed towards different targets.
> Wrap the entire output in JSON format.
user