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A systematic study on the role of SentiWordNet in opinion mining

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Abstract

Sentiment lexicons (SL) (aka lexical resources) are the repositories of one or several dictionaries that consist of known and precompiled sentiment terms. These lexicons play an important role in performing several different opinion mining tasks. The efficacy of the lexicon-based approaches in performing opinion mining (OM) tasks solely depends on selecting an appropriate opinion lexicon to analyze the text. Therefore, one has to explore the available sentiment lexicons and then select the most suitable resource. Among available resources, SentiWordNet (SWN) is the most widely used lexicon to perform tasks related to opinion mining. In SWN, each synset of WordNet is being assigned the three sentiment numerical scores; positive, negative and objective that are calculated using by a set of classifiers. In this paper, a detailed and comprehensive review of the work related to opinion mining using SentiWordNet is provided in a very distinctive way. This survey will be useful for the researchers contributing to the field of opinion mining. Following features make our contribution worthwhile and unique among the reviews of similar kind: (i) our review classifies the existing literature with respect to opinion mining tasks and subtasks (ii) it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels (word, sentences, document, aspect, clause, and concept levels) (iii) this state-of-art review covers each article in the following dimensions: the designated task performed, granularity level of the task completed, results obtained, and feature dimensions, and (iv) lastly it concludes the summary of the related articles according to the granularity levels, publishing years, related tasks (or subtasks), and types of classifiers used. In the end, major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed.

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Acknowledgements

This work was supported by the Department of Computer Science & IT, The Islamia University of Bahawalpur, Pakistan in collaboration with Laboratoire Informatique, Image et Interaction (L3i), University of La Rochelle, France.

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Correspondence to Malik Muhammad Saad Missen.

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Mujtaba Husnain is working as an assistant professor in the Department of Computer Science & IT, The Islamia University of Bahawalpur (IUB), Pakistan. He is also a PhD student from Department of Computer Sciences, The Islamia University of Bahawalpur, Pakistan. He completed his MS in 2006 with specialization in Machine Learning from School of Sciences and Technology, University of Management and Technology (UMT), Pakistan. He is the recipient of a number of awards, scholarships and research grants i.e., Higher Education Commission (HEC) Indigenous Scholarship for Higher Studies in 2005, PERIDOT scholarship for collaborative research study in Laboratoire Informatique, Image et Interaction (L3i) in La Rochelle, France in 2017 and 2018. His research areas are Data Visualization, Information retrieval, Image Processing and Machine Learning. He has been a university faculty member teaching graduate, post-graduate students, and supervising the research activities at graduate and post-graduate level.

Malik Muhammad Saad Missen is an assistant professor in Department of Computer Science, The Islamia University of Bahawalpur, Pakistan. He completed his PhD (Information Retrieval) Universite de Paul Sabatier, Toulouse France (Lab: Institu de Recherche en Informatique de Toulouse). He published a number of research articles on opinion mining and information retrieval. He has also received a number of local and international grants and scholarship for higher studies and research like Higher Education Commission scholarship for PhD in France, PERIDOT scholarship for collaborative research in France in 2016 and 2017. His area of research is information retrieval/processing, Web usability engineering, software quality assurance.

Nadeem Akhtar is working as an assistant professor at the Department of Computer Science & IT, The Islamia University of Bahawalpur (IUB), Pakistan. He has a PhD from Laboratory IRISA of Computer Science, University of South Brittany (UBS), European University of Brittany (UEB), Bretagne, France with honor “Tres Honorable”. He completed his MS (Master-2) with specialization in Information System Architecture from Institut Universitaire Professionnalisé (IUP), University of South Brittany, Bretagne, France. He is the recipient of a number of awards, scholarships and research grants i.e., Study in France 2004 French Embassy scholarship for Master studies, Higher Education Commission (HEC) overseas scholarship 2006 for PhD studies in France, Teaching assistant for ENSIBS — UBS France, HEC Start-up research grant of 0.5 million in 2012, Student research project grant from ICT in 2014. His research areas are formal validation, formal modeling, safety-critical systems, formal verification, multi-agent systems, system-of-systems and software architecture. He has been a university faculty member teaching graduate, post-graduate students, and supervising PhD and MS (Computer Science) research.

Mickaël Coustaty is with Faculties of Science and Technology, University of La Rochelle, France. He is an active member of computer societies like IEEE, ACM, CRA, etc. His area of research is document analysis, digital image processing, machine learning.

Shahzad Mumtaz is an assistant professor in Department of Computer Science, The Islamia University of Bahawalpur, Pakistan. He is PhD from Aston University, United Kingdom. He is an active researcher in area of Machine Learning and Data Visualization. He is author of several research articles published in quality journals. He is recipient of scholarship for PhD from Higher Education Commission, Pakistan. His area of interest is machine learning/data science/data mining/statistical pattern analysis in general but with a particular interest in probabilistic approaches of high-dimensional data projection approaches and their use in answering questions related to biological problems related to protein analytics, patient specific analytics, etc.

V. B. Surya Prasath is a mathematician with expertise in the application areas of image processing, computer vision, machine learning and data science. He received his PhD in mathematics from the Indian Institute of Technology Madras, India in 2009. He has been a postdoctoral fellow at the Department of Mathematics, University of Coimbra, Portugal, for two years from 2010 to 2011. From 2012 to 2015 he was with the Computational Imaging and VisAnalysis (CIVA) Lab at the University of Missouri, USA as a postdoctoral fellow, and from 2016 to 2017 as an assistant research professor. He is currently an assistant professor in the Division of Biomedical Informatics at the Cincinnati Children’s Hospital Medical Center, and at the Departments of Biomedical Informatics, Electrical Engineering and Computer Science, University of Cincinnati from 2018. He had summer fellowships/visits at Kitware Inc. NY, USA, The Fields Institute, Canada, and IPAM, University of California Los Angeles (UCLA), USA. His main research interests include nonlinear PDEs, regularization methods, inverse and ill-posed problems, variational, PDE based image processing, and computer vision with applications in remote sensing, biomedical imaging domains. His current research focuses are in data science, and bioimage informatics with machine learning techniques.

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Husnain, M., Missen, M.M.S., Akhtar, N. et al. A systematic study on the role of SentiWordNet in opinion mining. Front. Comput. Sci. 15, 154614 (2021). https://doi.org/10.1007/s11704-019-9094-0

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