Sentiment analysis of comments of wooden furniture based on naive Bayesian model

Ji, Jie; Wang, Haiyan; Song, Shasha; Pi, Jingwen

doi:10.1007/s13748-020-00221-3

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…The third is Naive-Bayes (NB) [41], a classification algorithm using the Bayes probability theorem to classify unlabeled observations. Several studies apply NB for several tasks, such as sentiment analysis [42], [43], chatbot [44] and opinion mining [45], [46]. The fourth is K-Nearest Neighbor K-NN [47], a simple machine-learning algorithm.…”

Section: Customer Need Phrases Classificationmentioning

confidence: 99%

Customer Needs Classification from Online Social Media Using Bag-of-Concepts Representation

Laosen,

Intana,

Chuaynukul

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Social media platforms are now a very powerful tool for digital marketing strategy because it helps companies to be in direct contact with their customers. A communication problem in digital social media is several customer needs phrases posted on social media, making it difficult for businesses to find relevant posts and respond to customers immediately. Therefore, knowing and understanding the customer requirements for a product can help the product owner to propose the right product to the right customer. This study focuses on understanding customer needs in Thai and classifying them into certain concepts. This study aims to classify customer needs for products in online social media community groups. The model focuses on understanding Thai customer need phrases. We then use a bag of concepts representation, including pattern analysis that applies n-grams together with POS and synonym replacement, conceptual analysis, pattern matching, and class labeling that applies concept sets obtained from the FP-Growth algorithm and represents TD-IDF value in a bag of concepts. The effectiveness of the proposed model is evaluated on five classification algorithms, including Decision Tree, Support Vector Machine, NaÃ¯ve-Bayes, K-Nearest Neighbor, and RBF Neural Network. The results show that Decision Tree can yield higher accuracy and F-measure values than the others. As this study is an initial step of a personalized product recommendation system in the future, this study will apply the model to the remaining domains for future work.

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Section: Customer Need Phrases Classificationmentioning

confidence: 99%

Customer Needs Classification from Online Social Media Using Bag-of-Concepts Representation

Laosen,

Intana,

Chuaynukul

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The application of machine learning has been prioritized in various fields, with the business environment as the main environment [14] [15]. In relation to what was said above, different agencies are being adapted in the application of machine learning for their different processes [8].…”

Section: Background and Related Workmentioning

confidence: 99%

Machine Learning for Feeling Analysis in Twitter Communications: A Case Study in HEYDRU!, Perú

Alegre-Veliz¹,

Gaspar-Ortiz²,

Gamboa-Cruzado

et al. 2022

Int. J. Interact. Mob. Technol.

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At present, sentiment analysis has become a trend; above all, in digital product development companies, as it is essential for rapid and automatic analysis. Sentiment analysis deals with emotions with the help of software, and it is playing an unavoidable role in workplaces. The constant growth of social networks, especially the Twitter social network, has made the ability to understand and comprehend users or clients take a greater scope regarding their needs; and therefore, increase the complexity of analysis of this social network, causing excessive expenses in time, personnel and money. This work presents a solution through the application of Machine Learning (ML) for sentiment analysis and thus improve analysis, execution time and customer satisfaction. The scope of this research is limited to using the Support Vector Machine (SVM) supervised learning technique for the intended analysis. The model derives from the ML technique making use of cross validation. The applied methodology is the CRISP-ML(Q) Methodology. The results show that the use of ML allows efficient sentiment analysis in Twitter communications.

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Automated sentimental analysis using heuristic-based CNN-BiLSTM for E-commerce dataset

Ramshankar¹,

M.²

2023

Data & Knowledge Engineering

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Sentiment analysis of comments of wooden furniture based on naive Bayesian model

Cited by 5 publications

References 11 publications

Customer Needs Classification from Online Social Media Using Bag-of-Concepts Representation

Customer Needs Classification from Online Social Media Using Bag-of-Concepts Representation

Machine Learning for Feeling Analysis in Twitter Communications: A Case Study in HEYDRU!, Perú

Automated sentimental analysis using heuristic-based CNN-BiLSTM for E-commerce dataset

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