Graph-Based Sentiment Analysis Model for E-Commerce Websites’ Data

Bordoloi, Monali; Biswas, Saroj Kumar

doi:10.1007/978-981-13-0617-4_45

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Yuan et al used a global decoder feedforward network to realize the recognition of multilanguage text, laying a foundation for complex text analysis [14]. Bordoloi et al designed an effective emotional analysis model, which carried out an advanced analysis on mobile phone comments collected by e-commerce sites based on the graph keyword extraction method [15]. Convolutional neural network (CNN) has been widely applied in many fields such as image recognition and NLP (for example, convolutional neural network [16] has been used to classify texts in social networks emotionally based on graph convolutional neural network [17].…”

Section: Introductionmentioning

confidence: 99%

A Sentiment Classification Model of E-Commerce User Comments Based on Improved Particle Swarm Optimization Algorithm and Support Vector Machines

Jiang

2022

Scientific Programming

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With the rapid increase of the number of Internet users and the amount of online comment data, a large number of referable information samples are provided for data mining technology. As a technical application of data mining, text sentiment classification can be widely used in public opinion management, marketing, and other fields. In this study, a combination approach to SVM (support vector machine) and IPSO (improved particle swarm optimization) is proposed to classify sentiment by using text data. First, the text data of 30,000 goods reviews and corresponding ratings are collected through the web crawler. Then, TFIDF (term frequency-inverse document frequency) and Word2vec are used to vectorize the goods review text data. Next, the proposed classification model is trained by the SVM, and the initial parameters of the SVM are optimized by the IPSO. Finally, we applied the trained SVM-IPSO model to the test set and evaluated the performance by several measures. Our experiment results indicate that the proposed model performed the best for text data sentiment classification. Additionally, the traditional machine learning model SVM becomes very effective after parameter optimization, which demonstrates that the parameters’ optimization by IPSO has successfully improved the classification accuracy. Furthermore, our proposed model SVM-IPSO significantly outperforms other benchmark models, indicating that it could be applied to improve the accuracy and efficiency for text data sentiment classification.

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Section: Introductionmentioning

confidence: 99%

A Sentiment Classification Model of E-Commerce User Comments Based on Improved Particle Swarm Optimization Algorithm and Support Vector Machines

Jiang

2022

Scientific Programming

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Bert-Based Text Keyword Extraction

Yili

Jia

Liu

2021

J. Phys.: Conf. Ser.

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With the explosive growth of network information, in order to obtain the information faster and more accurately, this paper proposes a text keyword extraction method based on Bert. Firstly, the key sentence set is extracted from the background material by Bert model as the information supplement to the text. Then, based on the extended text, TF-IDF, text rank and LDA are combined to extract keywords. The experimental results on real science and technology academic paper data sets show that the performance of the fusion multi type feature combination algorithm is better than that of the traditional single algorithm; and the F value of the algorithm is increased by 1.5% by extracting key sentences from background materials, which further improves the effect of key word extraction.

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