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- W2364295804 abstract "Analysis and monitoring of emotion information in micro-blog texts can help mine user behavior and offer the reference for the micro-blog public opinion supervision.However,micro-blog texts have the characteristics of short length,non-standardization,existence of a large number of anagrams and new words,etc.To classify micro-blog texts based on sentimental feature only lead poor accuracy.It is also difficult to meet practical demands.Therefore,a word stock of bigram collocation based on micro-blog corpus is constructed,and the PMI-IR-P algorithm is proposed to calculate the semantic weight of collocation based on PMI-IR algorithm.Combining the sentiment dictionary,micro-blog sentimental feature vector is generated by adopting statistical method.The C4.5 algorithm is used to establish classification models,so as to classify the sentiment polarity of the micro-blog.In the experiment,different data sets are utilized to construct collocation stock and classification models,and the result with the method based on sentiment dictionary is compared with rules as well as the Naive Bayes method.Experimental results show that with the help of C4.5 algorithm,the accuracy rate of micro-blog text sentiment classification reaches 87%,which has better effect." @default.
- W2364295804 created "2016-06-24" @default.
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- W2364295804 date "2014-01-01" @default.
- W2364295804 modified "2023-09-27" @default.
- W2364295804 title "Micro-blog Sentimental Feature Selection Based on Bigram Collocation" @default.
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