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Title: New techniques and framework for sentiment analysis and tuning of CRM structure in the context of Arabic language
Author: Al-Rubaiee, Hamed Saad
ISNI:       0000 0004 7961 4362
Awarding Body: University of Bedfordshire
Current Institution: University of Bedfordshire
Date of Award: 2018
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Knowing customers' opinions regarding services received has always been important for businesses. It has been acknowledged that both Customer Experience Management (CEM) and Customer Relationship Management (CRM) can help companies take informed decisions to improve their performance in the decision-making process. However, real-word applications are not so straightforward. A company may face hard decisions over the differences between the opinions predicted by CRM and actual opinions collected in CEM via social media platforms. Until recently, how to integrate the unstructured feedback from CEM directly into CRM, especially for the Arabic language, was still an open question. Furthermore, an accurate labelling of unstructured feedback is essential for the quality of CEM. Finally, CRM needs to be tuned and revised based on the feedback from social media to realise its full potential. However, the tuning mechanism for CEM of different levels has not yet been clarified. Facing these challenges, in this thesis, key techniques and a framework are presented to integrate Arabic sentiment analysis into CRM. First, as text pre-processing and classification are considered crucial to sentiment classification, an investigation is carried out to find the optimal techniques for the pre-processing and classification of Arabic sentiment analysis. Recommendations for using sentiment analysis classification in MSA as well as Saudi dialects are proposed. Second, to deal with the complexities of the Arabic language and to help operators identify possible conflicts in their original labelling, this study proposes techniques to improve the labelling process of Arabic sentiment analysis with the introduction of neural classes and relabelling. Finally, a framework for adjusting CRM via CEM for both the structure of the CRM system (on the sentence level) and the inaccuracy of the criteria or weights employed in the CRM system (on the aspect level) are proposed. To ensure the robustness and the repeatability of the proposed techniques and framework, the results of the study are further validated with real-word applications from different domains.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: opinion mining ; customer relationship management ; customer experience management ; sentiment analysis ; Twitter ; Arabic language ; N550 International Marketing