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

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

Sentiment Analysis

Unemployment

Technology

Machine Learning

Natural Language Processing

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.

Qaiser, Shahzad

Yusoff, Nooraini

Kabir Ahmad, Farzana

Ali, Ramsha

International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 07 (2020); pp. 88-103

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2020-05-06

Copyright (c) 2020 Shahzad Qaiser, Nooraini Yusoff, Ramsha Ali

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article