Construction of e-commerce major group course evaluation system based on CNN and LDA

Xiaoyu Wei

doi:10.1117/12.3034901

7 August 2024 Construction of e-commerce major group course evaluation system based on CNN and LDA

Xiaoyu Wei

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 132241T (2024) https://doi.org/10.1117/12.3034901
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

Analyzing course evaluation data is a key means to improve the quality of course construction for e-commerce majors. To this end, a course evaluation system for e-commerce majors based on convolutional neural network (CNN) and latent Dirichlet allocation (LDA) is constructed to realize the emotional tendency analysis of course evaluation and the acquisition of evaluation subject words. In terms of data collection, python crawler technology is used to obtain data. In terms of data preprocessing, Python's deduplication method and regular expression operations are used to complete data cleaning, and jieba is used to implement Chinese evaluation word segmentation. In terms of emotional tendency analysis, Word2Vec is used to convert text data into Word Embeddings, train and test the CNN model, and finally apply it to the emotional tendency analysis task. In term of sentiment topic analysis, TF-IDF is used to calculate the keywords of the evaluation data, and the LDA model is constructed to obtain the topics and subject words of the evaluation. Through experiments, it is found that the course evaluation system constructed in the article can realize the course evaluation of any e-commerce major group courses.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaoyu Wei "Construction of e-commerce major group course evaluation system based on CNN and LDA", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 132241T (7 August 2024); https://doi.org/10.1117/12.3034901

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KEYWORDS

Data modeling

Machine learning

Convolution

Deep learning

Emotion

Clouds

Education and training

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