Handling Imbalanced Datasets in the Case of Credit Card Fraud

Ounacer, Soumaya; Jihal, Houda; Bayoude, Kenza; Daif, Abderrahmane; Azzouazi, Mohamed

doi:10.1007/978-3-030-90633-7_56

Cited by 1 publication

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“…2) Performance measure: The choice of classifier for the current data is based on the performance measures [35]. The evaluation of the optimal solution in classification training can be defined based on the confusion matrix.…”

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confidence: 99%

Customer Sentiment Analysis in Hotel Reviews Through Natural Language Processing Techniques

Ounacer¹,

Mhamdi²,

Ardchir³

et al. 2023

IJACSA

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Customer reviews of products and services play a key role in the customers' decision to buy a product or use a service. Customers' preferences and choices are influenced by the opinions of others online; on blogs or social networks. New customers are faced with many views on the web, but they can't make the right decision. Hence, the need for sentiment analysis is to clarify whether opinions are positive, negative or neutral. This paper suggests using the Aspect-Based Sentiment Analysis approach on reviews extracted from tourism websites such as TripAdvisor and Booking. This approach is based on two main steps namely aspect extraction and sentiment classification related to each aspect. For aspect extraction, an approach based on topic modeling is proposed using the semi-supervised CorEx (Correlation Explanation) method for labeling word sequences into entities. As for sentiment classification, various supervised machine learning techniques are used to associate a sentiment (positive, negative or neutral) to a given aspect expression. Experiments on opinion corpora have shown very encouraging performances.

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confidence: 99%