An empirical review of challenges of and approaches to sentiment analysis
International Journal of Industrial Engineering and Production Research, ISSN: 2345-363X, Vol: 31, Issue: 2, Page: 317-322
2020
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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.
Article Description
Nowadays, data are food for the digital world. The main rich sources of data are generated by social networks such as Twitter, Facebook, Instagram, and LinkedIn. The data generated through microblogging services play a vital role in business intelligence such as product reviews, movie reviews, and election results prediction by social media data analysis. Sentiment Analysis (SA) is the key method of predicting netizens' emotions behind the text expressed in social media. The main objective of this survey is to shed light on the tools and techniques used in Sentiment Analysis and the relevant fields in brief detail.
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