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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (1): 95-103    DOI: 10.11925/infotech.2096-3467.2018.0158
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Fine-Grained Sentiment Analysis Based on Convolutional Neural Network
Hui Li,Yaqing Chai()
School of Economics and Management, Xidian University, Xi’an 710071, China
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[Objective] This paper proposes a fine-grained sentiment analysis method based on Convolutional Neural Network(CNN). [Methods] First, we incorporated attribute features into the word vector model. Then, we extracted the keyword sets of the comments statistically based on the fine-grained attributes of products or services. Third, we constructed the eigenvectors of the comments with attributes of the target objects. Finally, we trained the modified CNN model to add the affective clustering layer of the input text vector. [Results] Compared with the traditional emotion classification model, the training results of the new CNN model were significantly improved in terms of precision, recall and F-score. [Limitations] Only examined the new model with comments from one field. [Conclusions] The fine-grained sentiment analysis method based on convolutional neural network can dramatically improve the precision of sentiment classification.

Key wordsAttribute Feature      Word Vector      Sentiment Classification      CNN     
Received: 07 February 2018      Published: 04 March 2019

Cite this article:

Hui Li,Yaqing Chai. Fine-Grained Sentiment Analysis Based on Convolutional Neural Network. Data Analysis and Knowledge Discovery, 2019, 3(1): 95-103.

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