Subjectivity analysis in opinion mining - A systematic literature review

Basiron, Halizah and Kasmuri, Emaliana (2017) Subjectivity analysis in opinion mining - A systematic literature review. International Journal Of Advances In Soft Computing & Its Applications (Ijasca), 9 (3). pp. 132-159. ISSN 2074-8523

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Subjectivity analysis determines existence of subjectivity in text using subjective clues.It is the first task in opinion mining process.The difference between subjectivity analysis and polarity determination is the latter process subjective text to determine the orientation as positive or negative.There were many techniques used to solve the problem of segregating subjective and objective text.This paper used systematic literature review (SLR) to compile the undertaking study in subjective analysis.SLR is a literature review that collects multiple and critically analyse multiple studies to answer the research questions.Eight research questions were drawn for this purpose.Information such as technique,corpus,subjective clues representation and performance were extracted from 97 articles known as primary studies.This information was analysed to identify the strengths and weaknesses of the technique,affecting elements to the performance and missing elements from the subjectivity analysis.The SLR has found that majority of the study are using machine learning approach to identify and learn subjective text due to the nature of subjectivity analysis problem that is viewed as classification problem.The performance of this approach outperformed other approaches though currently it is at satisfactory level.Therefore,more studies are needed to improve the performance of subjectivity analysis.

Item Type: Article
Uncontrolled Keywords: opinion mining, sentiment analysis, subjectivity analysis, systematic literature review.
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HF Commerce
Divisions: Faculty of Information and Communication Technology
Depositing User: Mohd. Nazir Taib
Date Deposited: 31 Jan 2019 03:15
Last Modified: 12 Jul 2023 11:51
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