Temporal analysis of comparative opinion mining
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 10075 LNCS, Page: 311-322
2016
- 1Citations
- 14Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations1
- Citation Indexes1
- CrossRef1
- Captures14
- Readers14
- 14
Conference Paper Description
Social media have become a popular platform for people to share their opinions and emotions. Analyzing opinions that are posted on the web is very important since they influence future decisions of organizations and people. Comparative opinion mining is a subfield of opinion mining that deals with identifying and extracting information that is expressed in a comparative form. Due to the fact that there is a huge amount of opinions posted online everyday, analyzing comparative opinions from a temporal perspective is an important application that needs to be explored. This study introduces the idea of integrating temporal elements in comparative opinion mining. Different type of results can be obtained from the temporal analysis, including trend analysis, competitive analysis as well as burst detection. In our study we show that temporal analysis of comparative opinion mining provides more current and relevant information to users compared to standard opinion mining.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85006007393&origin=inward; http://dx.doi.org/10.1007/978-3-319-49304-6_36; http://link.springer.com/10.1007/978-3-319-49304-6_36; https://dx.doi.org/10.1007/978-3-319-49304-6_36; https://link.springer.com/chapter/10.1007/978-3-319-49304-6_36
Springer Science and Business Media LLC
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