Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers

Energy prices have gone up gradually since last year, but a drastic hike has been observed recently in the past couple of months, affecting people’s thrift. This, coupled with the load shedding and energy shortages in some parts of the world, led many to show anger and bitterness on the s...

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Main Authors: Zenun Kastrati, Ali Shariq Imran, Sher Muhammad Daudpota, Muhammad Atif Memon, Muhamet Kastrati
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10070760/
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author Zenun Kastrati
Ali Shariq Imran
Sher Muhammad Daudpota
Muhammad Atif Memon
Muhamet Kastrati
author_facet Zenun Kastrati
Ali Shariq Imran
Sher Muhammad Daudpota
Muhammad Atif Memon
Muhamet Kastrati
author_sort Zenun Kastrati
collection DOAJ
description Energy prices have gone up gradually since last year, but a drastic hike has been observed recently in the past couple of months, affecting people’s thrift. This, coupled with the load shedding and energy shortages in some parts of the world, led many to show anger and bitterness on the streets and on social media. Despite subsidies offered by many Governments to their citizens to compensate for high energy bills, the energy price hike is a trending topic on Twitter. However, not much attention is paid to opinion mining on social media posts on this topic. Therefore, in this study, we propose a solution that takes advantage of both a transformer-based sentiment analysis method and topic modeling to explore public engagement on Twitter regarding energy prices rising. The former method is employed to annotate the valence of the collected tweets as positive, neutral and negative, whereas the latter is used to discover hidden topics/themes related to energy prices for which people have expressed positive or negative sentiments. The proposed solution is tested on a dataset composed of 366,031 tweets collected from 01 January 2021 to 18 June 2022. The findings show that people have discussed a variety of topics which directly or indirectly affect energy prices. Moreover, the findings reveal that the public sentiment towards these topics has changed over time, in particular, in 2022 when negative sentiment was dominant.
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spelling doaj.art-510a6406c1aa4504852d93263b1610a62023-03-21T23:00:21ZengIEEEIEEE Access2169-35362023-01-0111265412655310.1109/ACCESS.2023.325728310070760Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With TransformersZenun Kastrati0https://orcid.org/0000-0002-0199-2377Ali Shariq Imran1https://orcid.org/0000-0002-2416-2878Sher Muhammad Daudpota2https://orcid.org/0000-0001-6684-751XMuhammad Atif Memon3Muhamet Kastrati4https://orcid.org/0000-0002-9919-4014Department of Informatics, Linnaeus University, Växjö, SwedenDepartment of Computer Science, Norwegian University of Science and Technology (NTNU), Gjøvik, NorwayDepartment of Computer Science, Sukkur IBA University, Sukkur, PakistanDepartment of Computer Science, Sukkur IBA University, Sukkur, PakistanDepartment of Computer Science, University of New York Tirana, Tirana, AlbaniaEnergy prices have gone up gradually since last year, but a drastic hike has been observed recently in the past couple of months, affecting people’s thrift. This, coupled with the load shedding and energy shortages in some parts of the world, led many to show anger and bitterness on the streets and on social media. Despite subsidies offered by many Governments to their citizens to compensate for high energy bills, the energy price hike is a trending topic on Twitter. However, not much attention is paid to opinion mining on social media posts on this topic. Therefore, in this study, we propose a solution that takes advantage of both a transformer-based sentiment analysis method and topic modeling to explore public engagement on Twitter regarding energy prices rising. The former method is employed to annotate the valence of the collected tweets as positive, neutral and negative, whereas the latter is used to discover hidden topics/themes related to energy prices for which people have expressed positive or negative sentiments. The proposed solution is tested on a dataset composed of 366,031 tweets collected from 01 January 2021 to 18 June 2022. The findings show that people have discussed a variety of topics which directly or indirectly affect energy prices. Moreover, the findings reveal that the public sentiment towards these topics has changed over time, in particular, in 2022 when negative sentiment was dominant.https://ieeexplore.ieee.org/document/10070760/Sentiment analysisenergy price hiketopic modelingtransformersBERTTwitter
spellingShingle Zenun Kastrati
Ali Shariq Imran
Sher Muhammad Daudpota
Muhammad Atif Memon
Muhamet Kastrati
Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers
IEEE Access
Sentiment analysis
energy price hike
topic modeling
transformers
BERT
Twitter
title Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers
title_full Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers
title_fullStr Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers
title_full_unstemmed Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers
title_short Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers
title_sort soaring energy prices understanding public engagement on twitter using sentiment analysis and topic modeling with transformers
topic Sentiment analysis
energy price hike
topic modeling
transformers
BERT
Twitter
url https://ieeexplore.ieee.org/document/10070760/
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