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Sentiment analysis method for comment text(PDF)


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Sentiment analysis method for comment text
Yang Yujuan1Yuan Huanhuan2Wang Yongli2
1.Nanjing Library,Nanjing 210018,China; 2.School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
comment text sentiment analysis term frequency-inverse document frequency long short-term memory attention
A weighted word2vec-attention long short-term memory(WWAL)emotion analysis model is proposed to overcome the shortcomings of traditional machine learning-based sentiment analysis algorithms of relying on manual establishment of emotional dictionary and manual intervention. The role of keywords in the comment text is highlighted. Word vectors based on word2vec are formed by introducing the term frequency-inverse document frequency(TFIDF)algorithm. An attention mechanism is introduced in the long short-term memory(LSTM)networks model. Experimental results on the standard dataset show the WWAL model is better than the traditional machine learning method in terms of precision,recall and F1 indicators.


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Last Update: 2019-06-30