A Check on Annotation in Sentiment Research
Fitrah Rumaisa1, Halizah Basiron2, Zurina Saaya3
1Fitrah Rumaisa, Department of Information Technology, Widyatama University, Indonesia.
2Halizah Basiron, Department of Information and Communications Technology, University Teknikal Malaysia Melaka (UTeM) Melaka, Malaysia.
3Zurina Saaya, Department of Information and Communications Technology, University Teknikal Malaysia Melaka (UTeM) Melaka, Malaysia.
Manuscript received on 21 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1346-1350 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B10650882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1065.0882S819
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The research literature on sentiment analysis methodologies has exponentially grown in recent years. In any research area, where new concepts and techniques are constantly introduced, it is, therefore, of interest to analyze the latest trends in this literature. In particular, we have chosen to primarily focus on the literature of the last five years, on annotation methodologies, including frequently used datasets and from which they were obtained. Based on the survey, it appears that researchers do more manual annotation in the formation of sentiment corpus. As for the dataset, there are still many uses of English language taken from social media such as Twitter. In this area of research, there are still many that need to be explored, such as the use of semi-automatic annotation method that is still very rarely used by researchers. Also, less popular languages, such as Malay, Korean, Japanese, and so on, still require corpus for sentiment analysis research.
Keywords: Survey, Sentiment-Annotated, Methodology, Dataset.
Scope of the Article: Data Warehousing