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      AI Based Emotion Detection for Textual Big Data: Techniques and Contribution

      , , , ,
      Big Data and Cognitive Computing
      MDPI AG

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          Abstract

          Online Social Media (OSM) like Facebook and Twitter has emerged as a powerful tool to express via text people’s opinions and feelings about the current surrounding events. Understanding the emotions at the fine-grained level of these expressed thoughts is important for system improvement. Such crucial insights cannot be completely obtained by doing AI-based big data sentiment analysis; hence, text-based emotion detection using AI in social media big data has become an upcoming area of Natural Language Processing research. It can be used in various fields such as understanding expressed emotions, human–computer interaction, data mining, online education, recommendation systems, and psychology. Even though the research work is ongoing in this domain, it still lacks a formal study that can give a qualitative (techniques used) and quantitative (contributions) literature overview. This study has considered 827 Scopus and 83 Web of Science research papers from the years 2005–2020 for the analysis. The qualitative review represents different emotion models, datasets, algorithms, and application domains of text-based emotion detection. The quantitative bibliometric review of contributions presents research details such as publications, volume, co-authorship networks, citation analysis, and demographic research distribution. In the end, challenges and probable solutions are showcased, which can provide future research directions in this area.

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          Most cited references44

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              Learning from imbalanced data: open challenges and future directions

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                Author and article information

                Contributors
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                Journal
                Big Data and Cognitive Computing
                BDCC
                MDPI AG
                2504-2289
                September 2021
                September 09 2021
                : 5
                : 3
                : 43
                Article
                10.3390/bdcc5030043
                00344b6d-b32c-498b-8247-68d29af31ee6
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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