Commodity Evaluation Analysis and Research Based on Deep Learning
LIU Zhipeng
HE Zhongshi
HE Weidong
ZHANG Hang
Abstract：This paper proposes an improved deep learning model for commodity evaluation sentiment analysis.Firstly,this pa-per uses stop words and tokenizer to pretreatment the data,then Skip-gram model is used to generate word vectors.Secondly,an au-togenerated sentiment lexicon is used to quantify the sentiment polarity of words in commodity reviews and integrate this information into the model input matrix.Lastly,this paper counts the differences between the network input through the distribution rules of de-signed and chose RNN or CNN for feature extraction. Above all is the Shunt-C&RNN commodity reviews sentiment classification model(improved deep learning approach).Compared with the traditional machine learning SVM and the single deep learning meth-od the proposed method has improved the precision by 6.6% and 1.5% respectively.
Keywords：deep learningnatural language processingword embiddingCNNRNNshuntsentiment analysis
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 921-927 )