Analytical mapping of opinion mining and sentiment analysis research during 2000–2015
| Autor: | D. Madhavi, Rajesh Piryani, Vivek Singh |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Information retrieval
Process (engineering) Computer science Sentiment analysis User-generated content Unstructured data 02 engineering and technology Library and Information Sciences Management Science and Operations Research Scientometrics Data science Computer Science Applications 020204 information systems 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Affective computing Citation Sentence Information Systems |
| Zdroj: | Information Processing & Management. 53:122-150 |
| ISSN: | 0306-4573 |
| DOI: | 10.1016/j.ipm.2016.07.001 |
| Popis: | The new transformed read-write Web has resulted in a rapid growth of user generated content on the Web resulting into a huge volume of unstructured data. A substantial part of this data is unstructured text such as reviews and blogs. Opinion mining and sentiment analysis (OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 2000–2016. For the scientometric mapping, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, types of authorship of papers on OMSA, collaboration patterns in publications on OMSA, most productive countries, institutions, journals and authors, citation patterns and an year-wise citation reference network, and theme density plots and keyword bursts in OMSA publications during the period. A somewhat detailed manual analysis of the data is also performed to identify popular approaches (machine learning and lexicon-based) used in these publications, levels (document, sentence or aspect-level) of sentiment analysis work done and major application areas of OMSA. The paper presents a detailed analytical mapping of OMSA research work and charts the progress of discipline on various useful parameters. |
| Databáze: | OpenAIRE |
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