Reviewing Sentiment Analysis at the Shallow End


  • Francisca Oladipo Federal University Lokoja
  • Ogunsanya, F. B Federal University Lokoja, Nigeria, 5University of Herdforshire, UK
  • Musa, A. E. Federal University Lokoja, Nigeria, 5University of Herdforshire, UK
  • Ogbuju, E. E Federal University Lokoja, Nigeria, 5University of Herdforshire, UK
  • Ariwa, E. Federal University Lokoja, Nigeria, 5University of Herdforshire, UK



sentiment analysis, classification, supervised machine learning, unsupervised machine learning


The social media space has evolved into a large labyrinth of information exchange platform and due to the growth in the adoption of different social media platforms, there has been an increasing wave of interests in sentiment analysis as a paradigm for the mining and analysis of users’ opinions and sentiments based on their posts. In this paper, we present a review of contextual sentiment analysis on social media entries with a specific focus on Twitter. The sentimental analysis consists of two broad approaches which are machine learning which uses classification techniques to classify text and is further categorized into supervised learning and unsupervised learning; and the lexicon-based approach which uses a dictionary without using any test or training data set, unlike the machine learning approach.



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How to Cite

Oladipo, F., F. B, O. ., A. E., M. ., E. E, O. ., & E., A. . (2020). Reviewing Sentiment Analysis at the Shallow End . Transactions on Engineering and Computing Sciences, 8(4), 47–62.