Sentiment Analysis of Impact of Technology on Employment from Text on Twitter

Authors

  • Shahzad Qaiser Department of Computer Science, Capital University of Science and Technology (CUST), Islamabad, Pakistan
  • Nooraini Yusoff Institute for Artificial Intelligence and Big Data (AIBIG), Universiti Malaysia Kelantan, City Campus, 16100 Kota Bharu, Kelantan, Malaysia
  • Farzana Kabir Ahmad School of Computing, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia
  • Ramsha Ali School of Quantitative Sciences, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia

DOI:

https://doi.org/10.3991/ijim.v14i07.10600

Keywords:

Sentiment Analysis, Unemployment, Technology, Machine Learning, Natural Language Processing

Abstract


Many different studies are in progress to analyze the content created by the users on social media due to its influence and social ripple effect. Various content created on social media has pieces of information and user’s sentiments about social issues. This study aims to analyze people’s sentiments about the impact of technology on employment and advancements in technologies and build a machine learning classifier to classify the sentiments. People are getting nervous, depressed and even doing suicides due to unemployment; hence, it is essential to explore this relatively new area of research. The study has two main objectives 1) to preprocess text collected from Twitter concerning the impact of technology on employment and analyze its sentiment, 2) to evaluate the performance of machine learning Naïve Bayes (NB) classifier on the text. To achieve this, a methodology is proposed that includes 1) data collection and preprocessing 2) analyze sentiment, 3) building machine learning classifier and 4) compare the performance of NB and support vector machine (SVM). NB and SVM achieved 87.18% and 82.05% accuracy respectively. The study found that 65% of the people hold negative sentiment regarding the impact of technology on employment and technological advancements; hence people must acquire new skills to minimize the effect of structural unemployment.

Author Biographies

Shahzad Qaiser, Department of Computer Science, Capital University of Science and Technology (CUST), Islamabad, Pakistan

Lecturer at Department of Computer Science, Capital University of Science and Technology (CUST), Islamabad, Pakistan

Nooraini Yusoff, Institute for Artificial Intelligence and Big Data (AIBIG), Universiti Malaysia Kelantan, City Campus, 16100 Kota Bharu, Kelantan, Malaysia

Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan (Jeli Campus), 17600 Jeli, Kelantan, Malaysia

Farzana Kabir Ahmad, School of Computing, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia

Senior Lecturer at School of Computing, Awang Had Salleh Graduate School of Arts & Sciences, Universiti Utara Malaysia

Ramsha Ali, School of Quantitative Sciences, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia

Master of Science (Decision Science) student at School of Quantitative Sciences, Awang Had Salleh Graduate School of Arts & Sciences, Universiti Utara Malaysia

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Published

2020-05-06

How to Cite

Qaiser, S., Yusoff, N., Kabir Ahmad, F., & Ali, R. (2020). Sentiment Analysis of Impact of Technology on Employment from Text on Twitter. International Journal of Interactive Mobile Technologies (iJIM), 14(07), pp. 88–103. https://doi.org/10.3991/ijim.v14i07.10600

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Section

Papers