Sentiment analysis of hotel online reviews using the BERT model and ERNIE model—Data from China

Yu, Wen; Liang, Yezhang; Zhu, Xinhua

doi:10.1371/journal.pone.0275382

Cited by 20 publications

(2 citation statements)

References 30 publications

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“…Contemporary studies have explored sentiment analysis within the context of comments or reviews, covering various areas such as online courses [10], Amazon product reviews [11], film reviews [12], hotel online reviews [13], online product reviews [14,15],…”

Section: Introductionmentioning

confidence: 99%

Mitigating Class Imbalance in Sentiment Analysis through GPT-3-Generated Synthetic Sentences

Suhaeni,

Yong

2023

Applied Sciences

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In this paper, we explore the effectiveness of the GPT-3 model in tackling imbalanced sentiment analysis, focusing on the Coursera online course review dataset that exhibits high imbalance. Training on such skewed datasets often results in a bias towards the majority class, undermining the classification performance for minority sentiments, thereby accentuating the necessity for a balanced dataset. Two primary initiatives were undertaken: (1) synthetic review generation via fine-tuning of the Davinci base model from GPT-3 and (2) sentiment classification utilizing nine models on both imbalanced and balanced datasets. The results indicate that good-quality synthetic reviews substantially enhance sentiment classification performance. Every model demonstrated an improvement in accuracy, with an average increase of approximately 12.76% on the balanced dataset. Among all the models, the Multinomial Naïve Bayes achieved the highest accuracy, registering 75.12% on the balanced dataset. This study underscores the potential of the GPT-3 model as a feasible solution for addressing data imbalance in sentiment analysis and offers significant insights for future research.

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Section: Introductionmentioning

confidence: 99%

Mitigating Class Imbalance in Sentiment Analysis through GPT-3-Generated Synthetic Sentences

Suhaeni,

Yong

2023

Applied Sciences

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“…Further improvements have been achieved with the emergence of bidirectional encoder representations from transformers (BERT), which is a language representation model based on transformers for pre-training deep bidirectional representations from unlabeled text [15]. BERT has been used in many natural language processing (NLP) use cases, including sentiment analysis [16], text summarization [17], question answering [18], and text classification [19].…”

Section: Introductionmentioning

confidence: 99%

Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques

Alhazzani,

Al-Turaiki,

Alkhodair

2023

Applied Sciences

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Improving the quality of healthcare services is of the utmost importance in healthcare systems. Patient experience is a key aspect that should be gauged and monitored continuously. However, the measurement of such a vital indicator typically cannot be carried out directly, instead being derived from the opinions of patients who usually express their experience in free text. When it comes to patient comments written in the Arabic language, the currently used strategy to classify Arabic comments is totally reliant on human annotation, which is time-consuming and prone to subjectivity and error. Thus, fully using the value of patient feedback in a timely manner is difficult. This paper addresses the problem of classifying patient experience (PX) comments written in Arabic into 25 classes by using deep learning- and BERT-based models. A real-world data set of patient comments is obtained from the Saudi Ministry of Health for this purpose. Features are extracted from the data set, then used to train deep learning-based classifiers—including BiLSTM and BiGRU—for which pre-trained static word embedding and pre-training vector word embeddings are utilized. Furthermore, we utilize several Arabic pre-trained BERT models, in addition to building PX_BERT, a customized BERT model using the PX unlabeled database. From the experimental results for the 28 classifiers built in this study, the best-performing models (based on the F1 score) are found to be PX_BERT and AraBERTv02. To the best of our knowledge, this is the first study to tackle PX comment classification for the Arabic language.

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AI‐Driven Sentiment Analysis for Retail Management: A Graph‐Based DSS Comparing Franchise and Company‐Owned Stores

Baray,

Cliquet

2024

Manage Decis Econ

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This paper introduces an AI‐driven Decision Support System (DSS) for sentiment analysis of customer reviews in Starbucks UK. The methodology involves three main steps: collecting customer reviews from trusted sources, applying AI‐driven preprocessing techniques to extract key attributes, and using Graph Machine Learning techniques to unveil customer satisfaction. A new Graph‐Based Sentiment Analysis Algorithm is proposed to extract object–sentiment pairs from each comment and model relationships through a graph‐based approach. Results indicate a superior performance in terms of accuracy and efficiency compared to cell‐based methods. The analysis identifies drivers of customer satisfaction, including value for money, quality experience, and ambiance.

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Sentiment analysis of hotel online reviews using the BERT model and ERNIE model—Data from China

Cited by 20 publications

References 30 publications

Mitigating Class Imbalance in Sentiment Analysis through GPT-3-Generated Synthetic Sentences

Mitigating Class Imbalance in Sentiment Analysis through GPT-3-Generated Synthetic Sentences

Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques

AI‐Driven Sentiment Analysis for Retail Management: A Graph‐Based DSS Comparing Franchise and Company‐Owned Stores

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