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Techniques and Trends for Fine-Grained Opinion Mining and Sentiment Analysis: Recent Survey

[ Vol. 13 , Issue. 2 ]


Dalila Bouras*, Mohamed Amroune, Hakim Bendjenna and Nabiha Azizi   Pages 215 - 227 ( 13 )


<p>Background: Nowadays, with the appearance of web 2.0, more users express their opinions, judgments, and thoughts towards certain objects, services, organizations, and their attributes via social networking, forum entries, websites, and blogs and so on. In this way, the volume of raw content generated by these users will increase rapidly with enormous size, where people often find difficulties in identifying and summarizing fine-grained sentiments buried in the opinion-rich resources. The traditional opinion mining techniques, which focused on the overall sentiment of the review, fails to uncover the sentiments expressed on the aspects of the reviewed entity. For that, researchers in Aspect-based opinion mining community try to solve and handle this problem. </P><P> Objectives: Our proposed study aims to present, survey and compare in the first place the important recent Aspect-based opinion mining approaches relevant to important languages such English, Arabic and Chinese and commonly datasets used in literature so that future researchers could improve their results. The cited approaches used the last techniques in the area on Opinion mining field, relevant to the Deep Learning models. In the second place, we try to highlight and give special attention to the Arabic language by introducing a dashboard of deep learning methods dedicated to the Arabic language. Finally, we emphasize the research gaps and future challenges in both English and Arabic languages that provide some new potential research fields. </P><P> Methods: We have carefully summarized 48 models according to their algorithm into three categories: supervised, semi-supervised and unsupervised. Due to a large number of approaches with diverse datasets and techniques, we propose some statistical graphics to compare different experimentation results namely precision, Recall, and F-measure. Also, the study has conferred a comparative analysis and a comprehensive discussion of different approaches and techniques dedicated to the aspect extraction sub-task using the new tendency that of deep learning on both Arabic, English and Chinese language. We have introduced some future challenges, research gaps, and new trends in the opinion mining task, which need more efforts and investigations to present new solutions that make the opinion mining field more pervasive and give more ideas about the different researches done in the field of OM. </P><P> Conclusion: We have compared the different approaches and techniques dedicated to the extraction of aspects using the new tendency that of deep learning. Our contribution illustrates the add values given by deep learning models in the treatment of user reviews expressed in the Arabic language. At the same time, this work is mainly based on the use of the evaluation performance metrics (precision, recall, and f-measure).</p>


Sentiment analysis, opinion mining, aspect extraction, deep learning, arabic, opinion mining.


Laboratory of Mathematics, Informatics and Systems (LAMIS), University of Laarbi Tebessi, Tebessa, Laboratory of Mathematics, Informatics and Systems (LAMIS), University of Laarbi Tebessi, Tebessa, Laboratory of Mathematics, Informatics and Systems (LAMIS), University of Laarbi Tebessi, Tebessa, University of Annaba, Annaba

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