Polarity of Yelp Reviews: A BERT–LSTM Comparative Study
With the rapid growth in social network comments, the need for more effective methods to classify their polarity—negative, neutral, or positive—has become essential. Sentiment analysis, powered by natural language processing, has evolved significantly with the adoption of advanced deep learning tech...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/9/5/140 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850257393686937600 |
|---|---|
| author | Rachid Belaroussi Sié Cyriac Noufe Francis Dupin Pierre-Olivier Vandanjon |
| author_facet | Rachid Belaroussi Sié Cyriac Noufe Francis Dupin Pierre-Olivier Vandanjon |
| author_sort | Rachid Belaroussi |
| collection | DOAJ |
| description | With the rapid growth in social network comments, the need for more effective methods to classify their polarity—negative, neutral, or positive—has become essential. Sentiment analysis, powered by natural language processing, has evolved significantly with the adoption of advanced deep learning techniques. Long Short-Term Memory networks capture long-range dependencies in text, while transformers, with their attention mechanisms, excel at preserving contextual meaning and handling high-dimensional, semantically complex data. This study compares the performance of sentiment analysis models based on LSTM and BERT architectures using key evaluation metrics. The dataset consists of business reviews from the Yelp Open Dataset. We tested LSTM-based methods against BERT and its variants—RoBERTa, BERTweet, and DistilBERT—leveraging popular pipelines from the Hugging Face Hub. A class-by-class performance analysis is presented, revealing that more complex BERT-based models do not always guarantee superior results in the classification of Yelp reviews. Additionally, the use of bidirectionality in LSTMs does not necessarily lead to better performance. However, across a diversity of test sets, transformer models outperform traditional RNN-based models, as their generalization capability is greater than that of a simple LSTM model. |
| format | Article |
| id | doaj-art-3c540bedf5d04e298e31c65ab09252ca |
| institution | OA Journals |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-3c540bedf5d04e298e31c65ab09252ca2025-08-20T01:56:25ZengMDPI AGBig Data and Cognitive Computing2504-22892025-05-019514010.3390/bdcc9050140Polarity of Yelp Reviews: A BERT–LSTM Comparative StudyRachid Belaroussi0Sié Cyriac Noufe1Francis Dupin2Pierre-Olivier Vandanjon3COSYS-GRETTIA, University Gustave Eiffel, F-77447 Marne-la-Vallée, FranceCOSYS-GRETTIA, University Gustave Eiffel, F-77447 Marne-la-Vallée, FranceCOSYS-GRETTIA, University Gustave Eiffel, F-77447 Marne-la-Vallée, FranceAME-SPLOTT, University Gustave Eiffel, All. des Ponts et Chaussées, F-44340 Bouguenais, FranceWith the rapid growth in social network comments, the need for more effective methods to classify their polarity—negative, neutral, or positive—has become essential. Sentiment analysis, powered by natural language processing, has evolved significantly with the adoption of advanced deep learning techniques. Long Short-Term Memory networks capture long-range dependencies in text, while transformers, with their attention mechanisms, excel at preserving contextual meaning and handling high-dimensional, semantically complex data. This study compares the performance of sentiment analysis models based on LSTM and BERT architectures using key evaluation metrics. The dataset consists of business reviews from the Yelp Open Dataset. We tested LSTM-based methods against BERT and its variants—RoBERTa, BERTweet, and DistilBERT—leveraging popular pipelines from the Hugging Face Hub. A class-by-class performance analysis is presented, revealing that more complex BERT-based models do not always guarantee superior results in the classification of Yelp reviews. Additionally, the use of bidirectionality in LSTMs does not necessarily lead to better performance. However, across a diversity of test sets, transformer models outperform traditional RNN-based models, as their generalization capability is greater than that of a simple LSTM model.https://www.mdpi.com/2504-2289/9/5/140sentiment analysisnatural language processingLSTMBERTdeep learningpolarity detection |
| spellingShingle | Rachid Belaroussi Sié Cyriac Noufe Francis Dupin Pierre-Olivier Vandanjon Polarity of Yelp Reviews: A BERT–LSTM Comparative Study Big Data and Cognitive Computing sentiment analysis natural language processing LSTM BERT deep learning polarity detection |
| title | Polarity of Yelp Reviews: A BERT–LSTM Comparative Study |
| title_full | Polarity of Yelp Reviews: A BERT–LSTM Comparative Study |
| title_fullStr | Polarity of Yelp Reviews: A BERT–LSTM Comparative Study |
| title_full_unstemmed | Polarity of Yelp Reviews: A BERT–LSTM Comparative Study |
| title_short | Polarity of Yelp Reviews: A BERT–LSTM Comparative Study |
| title_sort | polarity of yelp reviews a bert lstm comparative study |
| topic | sentiment analysis natural language processing LSTM BERT deep learning polarity detection |
| url | https://www.mdpi.com/2504-2289/9/5/140 |
| work_keys_str_mv | AT rachidbelaroussi polarityofyelpreviewsabertlstmcomparativestudy AT siecyriacnoufe polarityofyelpreviewsabertlstmcomparativestudy AT francisdupin polarityofyelpreviewsabertlstmcomparativestudy AT pierreoliviervandanjon polarityofyelpreviewsabertlstmcomparativestudy |