Research on Sentiment Analysis Method of English Writing Based on NLP and Machine Learning

Yunong Tian


In response to the demand for sentiment analysis in modern cultural studies, the article conducts in depth research on the word vector generation and training methods in Natural Language Processing (NLP). By adopting the hierarchical Sotfmax structure, the problem of matrix sparseness caused by the increase of the vector dimension in word vector description is alleviated. The CNN⁃Softmax model mentioned in the article has a significant improvement in performance due to the introduction of a deeper convolution structure. Accuracy and  have reached 83.8% and 81.9%,respectively, which is about 4% higher than the traditional binary tree⁃ based model.


Emotion Analysis; Deep Learning; Convolutional Neural Network; NLP

Full Text:


Included Database


Huang SS, Liao WJ. Research on Text Classification Based on Mixed Features [J]. Electronic Design Engineering,2019,27(7):61-65.

Jiang MQ, Li YW, Liu H, et al. Research on Attribute-level Sentiment Classification for Question Answering Text [J]. Computer Science, 2019, 46(S2):5-8.

Zhai SP, Yang YY, Qiu C, et al. Based on attention mechanism Bi LSTM algorithm of bilingual text sentiment analysis [J]. Computer Applications and Software,2019,36(12):251-255.

Zhang ZN. Design and Implementation of Chinese Text Classification System Based on SVM [J]. Electronic Design Engineering,2016,24(16):139-141.

Yang KM, Wu MF, Chen T. A survey of Generalized Text Sentiment Analysis [J]. Journal of Computer Applications, 2019, 39(S2):6-14.

Han JS, Chen J, Chen P, et al. Chinese text Sentiment classification based on two-way time Deep Convolutional network [J]. Computer Applications and Software, 2019,36(12):225-231.

Xu Z. Neural network model based on emotion information fusion for short text sentiment classification [D]. Jinan: Shandong University,2019.

Zhao L, Mai FJ, Zhang XW. Research on Multi-feature Fusion Voting-SRM Sentiment Classification [J]. Journal of Small Microcomputer Systems, 2019, 40(11): 2269-2273.



  • There are currently no refbacks.

Copyright (c) 2023 Yunong Tian

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.