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Improved Sentiment Urgency Emotion Detection for Business Intelligence

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Book cover Advances in Intelligent Networking and Collaborative Systems (INCoS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1263))

Abstract

The impact of social media on people’s lives has significantly grown over the last decade. Individuals use it to promote discussions and a way of acquiring data. Industries use social media to market their goods and facilities, advise and inform clients about future offers, and follow up on their direct market. It also offers vital information concerning the general emotions and sentiments directly connected to welfare and security. In this work, an improved model called Improved Sentiment Urgency Emotion Detection (ISUED) has been created based on previous work for opinion and social media mining implemented with Multinomial Naive Bayes algorithm and based on three classifiers which are sentiment analysis, urgency detection, and emotion classification. The model will be trained to improve its accuracy and F1 score so that the precision and percentage of correctly predicted texts is elevated. This model will be applied on the same data set of previous work acquired from a general business Twitter account of one of the largest chains of supermarkets in the United Kingdom to be able to see what sentiments and emotions can be detected and how urgent they are.

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Authors and Affiliations

  1. School of Computing, Edge Hill University, Ormskirk, UK

    Tariq Soussan & Marcello Trovati

Authors
  1. Tariq Soussan
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  2. Marcello Trovati
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Corresponding author

Correspondence to Tariq Soussan .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada

    Kin Fun Li

  3. School of Science and Technology, Kwansei Gakuin University, Sanda, Japan

    Hiroyoshi Miwa

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Soussan, T., Trovati, M. (2021). Improved Sentiment Urgency Emotion Detection for Business Intelligence. In: Barolli, L., Li, K., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2020. Advances in Intelligent Systems and Computing, vol 1263. Springer, Cham. https://doi.org/10.1007/978-3-030-57796-4_30

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