Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification
The increasing number of online product and service reviews has created a substantial information resource for individuals and businesses. Automatic review summarization helps overcome information overload. Research in automatic text summarization shows remarkable advancement. However, research on A...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/21/10980 |
| _version_ | 1797469209855262720 |
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| author | Ghada Amoudi Amal Almansour Hanan Saleh Alghamdi |
| author_facet | Ghada Amoudi Amal Almansour Hanan Saleh Alghamdi |
| author_sort | Ghada Amoudi |
| collection | DOAJ |
| description | The increasing number of online product and service reviews has created a substantial information resource for individuals and businesses. Automatic review summarization helps overcome information overload. Research in automatic text summarization shows remarkable advancement. However, research on Arabic text summarization has not been sufficiently conducted. This study proposes an extractive Arabic review summarization approach that incorporates the reviews’ polarity and sentiment aspects and employs a graph-based ranking algorithm, TextRank. We demonstrate the advantages of the proposed methods through a set of experiments using hotel reviews from Booking.com. Reviews were grouped based on their polarity, and then TextRank was applied to produce the summary. Results were evaluated using two primary measures, <i>BLEU</i> and <i>ROUGE</i>. Further, two Arabic native speakers’ summaries were used for evaluation purposes. The results showed that this approach improved the summarization scores in most experiments, reaching an F1 score of 0.6294. Contributions of this work include applying a graph-based approach to a new domain, Arabic hotel reviews, adding sentiment dimension to summarization, analyzing the algorithms of the two primary summarization metrics showing the working of these measures and how they could be used to give accurate results, and finally, providing four human summaries for two hotels which could be utilized for another research. |
| first_indexed | 2024-03-09T19:18:08Z |
| format | Article |
| id | doaj.art-6f7538fbd3704976988f7bf9223be067 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:18:08Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6f7538fbd3704976988f7bf9223be0672023-11-24T03:36:01ZengMDPI AGApplied Sciences2076-34172022-10-0112211098010.3390/app122110980Improved Graph-Based Arabic Hotel Review Summarization Using Polarity ClassificationGhada Amoudi0Amal Almansour1Hanan Saleh Alghamdi2Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaThe increasing number of online product and service reviews has created a substantial information resource for individuals and businesses. Automatic review summarization helps overcome information overload. Research in automatic text summarization shows remarkable advancement. However, research on Arabic text summarization has not been sufficiently conducted. This study proposes an extractive Arabic review summarization approach that incorporates the reviews’ polarity and sentiment aspects and employs a graph-based ranking algorithm, TextRank. We demonstrate the advantages of the proposed methods through a set of experiments using hotel reviews from Booking.com. Reviews were grouped based on their polarity, and then TextRank was applied to produce the summary. Results were evaluated using two primary measures, <i>BLEU</i> and <i>ROUGE</i>. Further, two Arabic native speakers’ summaries were used for evaluation purposes. The results showed that this approach improved the summarization scores in most experiments, reaching an F1 score of 0.6294. Contributions of this work include applying a graph-based approach to a new domain, Arabic hotel reviews, adding sentiment dimension to summarization, analyzing the algorithms of the two primary summarization metrics showing the working of these measures and how they could be used to give accurate results, and finally, providing four human summaries for two hotels which could be utilized for another research.https://www.mdpi.com/2076-3417/12/21/10980<i>BLEU</i>classificationdeep learningextractivenatural language processing<i>ROUGE</i> |
| spellingShingle | Ghada Amoudi Amal Almansour Hanan Saleh Alghamdi Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification Applied Sciences <i>BLEU</i> classification deep learning extractive natural language processing <i>ROUGE</i> |
| title | Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification |
| title_full | Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification |
| title_fullStr | Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification |
| title_full_unstemmed | Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification |
| title_short | Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification |
| title_sort | improved graph based arabic hotel review summarization using polarity classification |
| topic | <i>BLEU</i> classification deep learning extractive natural language processing <i>ROUGE</i> |
| url | https://www.mdpi.com/2076-3417/12/21/10980 |
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