Predicting user preferences on changing trends and innovations using SVM based sentiment analysis

Chidambarathanu, K.; Shunmuganathan, K. L.

doi:10.1007/s10586-017-1505-0

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Support vector machine (SVM) is a nonlinear mapping classifier that maps the input vector to a high-dimensional space and constructs the optimal classification hyperplane to separate the samples of different categories [32]. Compared with supervised learning such as Logistic Regression and Naive Bayes, it has better model accuracy in dealing with binary classification problems [33].…”

Section: Distributed Sentiment Analysis Based On Word2vecmentioning

confidence: 99%

The Insights, “Comfort” Effect and Bottleneck Breakthrough of “E-Commerce Temperature” during the COVID-19 Pandemic

Yang

et al. 2022

JTAER

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The impact of the COVID-19 pandemic on fresh food e-commerce has led to a loss of consumers, and “e-commerce temperature” is seen as an important means of alleviating consumer dissatisfaction and retaining consumers. To explore the connotation and effect of it, and to break through possible “comfort” bottlenecks, we used online reviews of the Jingdong fresh food platform as research data, mined the characteristics of “e-commerce temperature” with the help of the LDA topic model, and evaluated the mechanism of “e-commerce temperature” on consumer satisfaction during the pandemic by using quasi-natural experiments and Word2vec-based sentiment analysis. The results show that “e-commerce temperature” has five connotations of logistics commitment, humanized delivery, health pledge, pandemic perseverance, and consumer care, which can effectively mitigate the loss of consumer satisfaction. Interestingly, we found that the “e-commerce temperature” has a limited “comfort” effect. Additionally, further social network analysis shows that the bottleneck is mainly due to the consumers’ psychological gaps when comparing the usual e-commerce services, and cretailers can repair them through financial compensation and spiritual solace. The study explores e-commerce service quality at different pandemic stages with the help of text mining techniques, enriches the theory of e-commerce research, and alleviates the Hawthorne bias in traditional empirical studies. This study also provides a reference for e-retailers to improve service quality and respond to emergencies in a changing post-pandemic era.

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Section: Distributed Sentiment Analysis Based On Word2vecmentioning

confidence: 99%

The Insights, “Comfort” Effect and Bottleneck Breakthrough of “E-Commerce Temperature” during the COVID-19 Pandemic

Yang

et al. 2022

JTAER

View full text Add to dashboard Cite

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“…The parameter named as F-score has been measured. Chidambarathanu et al [8] proposed SVM (Support vector machine) classification approach that assists the clients to create informed communication. Classifier used the social media data so that accurate decision has been takes place.…”

Section: Related Workmentioning

confidence: 99%

News recommendation system using machine learning

Rani

Pathak

2018

IJET

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The forecasting of financial news is yet becoming the main issue to divide the new into different classes on the basis of present time series. Moreover, it might be utilized for predicting and analyzing the stock market for the particular industry. Thus, the new content is significantly important to influence market forecast report. In this paper, the financial news from four countries namely America, Australia, India and South Africa along with their stop words are consider. The words along with their weighted values are determined and then the neural network is trained. Here, artificial neural network is used for classifying the appropriate results for the given input data. At last the comparison of ANN with SVM is shown. Experiments show that the ANN classification provides high accuracy to predict the news than the SVM classifier.

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A NLP framework based on meaningful latent-topic detection and sentiment analysis via fuzzy lattice reasoning on youtube comments

Jelodar

Wang

Rabbani

et al. 2020

Multimed Tools Appl

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Predicting user preferences on changing trends and innovations using SVM based sentiment analysis

Cited by 5 publications

References 17 publications

The Insights, “Comfort” Effect and Bottleneck Breakthrough of “E-Commerce Temperature” during the COVID-19 Pandemic

The Insights, “Comfort” Effect and Bottleneck Breakthrough of “E-Commerce Temperature” during the COVID-19 Pandemic

News recommendation system using machine learning

A NLP framework based on meaningful latent-topic detection and sentiment analysis via fuzzy lattice reasoning on youtube comments

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