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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Veröffentlicht in:Applied sciences Jg. 12; H. 21; S. 10980
Hauptverfasser: Amoudi, Ghada, Almansour, Amal, Alghamdi, Hanan Saleh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.11.2022
Schlagworte:
Arabic language
BLEU
classification
Deep learning
extractive
Fuzzy logic
Information sources
Methods
Natural language processing
ROUGE
Semantic web
Semantics
User generated content
Web 2.0
ISSN:2076-3417, 2076-3417
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Zusammenfassung: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, BLEU and ROUGE. 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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app122110980