Leveraging AI and Sentiment Analysis for Forecasting Election Outcomes in Mauritius

• URL: http://arxiv.org/abs/2410.20859v1
• Date: Mon, 28 Oct 2024 09:21:15 GMT
• Title: Leveraging AI and Sentiment Analysis for Forecasting Election Outcomes in Mauritius
• Authors: Missie Chercheur, Malkenzie Bovafiz,
• Abstract summary: This study explores the use of AI-driven sentiment analysis as a novel tool for forecasting election outcomes, focusing on Mauritius' 2024 elections. We analyze media sentiment toward two main political parties L'Alliance Lepep and L'Alliance Du Changement by classifying news articles from prominent Mauritian media outlets as positive, negative, or neutral. Findings indicate that positive media sentiment strongly correlates with projected electoral gains, underscoring the role of media in shaping public perception.
• Score: 0.0
• License:
• Abstract: This study explores the use of AI-driven sentiment analysis as a novel tool for forecasting election outcomes, focusing on Mauritius' 2024 elections. In the absence of reliable polling data, we analyze media sentiment toward two main political parties L'Alliance Lepep and L'Alliance Du Changement by classifying news articles from prominent Mauritian media outlets as positive, negative, or neutral. We employ a multilingual BERT-based model and a custom Sentiment Scoring Algorithm to quantify sentiment dynamics and apply the Sentiment Impact Score (SIS) for measuring sentiment influence over time. Our forecast model suggests L'Alliance Du Changement is likely to secure a minimum of 37 seats, while L'Alliance Lepep is predicted to obtain the remaining 23 seats out of the 60 available. Findings indicate that positive media sentiment strongly correlates with projected electoral gains, underscoring the role of media in shaping public perception. This approach not only mitigates media bias through adjusted scoring but also serves as a reliable alternative to traditional polling. The study offers a scalable methodology for political forecasting in regions with limited polling infrastructure and contributes to advancements in the field of political data science.

Related papers

• On the Use of Proxies in Political Ad Targeting [49.61009579554272]
  We show that major political advertisers circumvented mitigations by targeting proxy attributes. Our findings have crucial implications for the ongoing discussion on the regulation of political advertising.
  arXiv  Detail & Related papers  (2024-10-18T17:15:13Z)
• Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
  This study seeks to identify and quantify biases in simulating political samples with Large Language Models. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
  arXiv  Detail & Related papers  (2024-07-16T05:52:26Z)
• Can LLMs Help Predict Elections? (Counter)Evidence from the World's Largest Democracy [3.0915192911449796]
  The study of how social media affects the formation of public opinion and its influence on political results has been a popular field of inquiry. We introduce a new method: harnessing the capabilities of Large Language Models (LLMs) to examine social media data and forecast election outcomes.
  arXiv  Detail & Related papers  (2024-05-13T15:13:23Z)
• Whose Side Are You On? Investigating the Political Stance of Large Language Models [56.883423489203786]
  We investigate the political orientation of Large Language Models (LLMs) across a spectrum of eight polarizing topics. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. The findings suggest that users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
  arXiv  Detail & Related papers  (2024-03-15T04:02:24Z)
• Prediction of the 2023 Turkish Presidential Election Results Using Social Media Data [0.5156484100374059]
  We aim to predict the vote shares of parties participating in the 2023 elections in Turkey by combining social media data with traditional polling data. Our approach is a volume-based approach that considers the number of social media interactions rather than content.
  arXiv  Detail & Related papers  (2023-05-28T13:17:51Z)
• Computational Assessment of Hyperpartisanship in News Titles [55.92100606666497]
  We first adopt a human-guided machine learning framework to develop a new dataset for hyperpartisan news title detection. Overall the Right media tends to use proportionally more hyperpartisan titles. We identify three major topics including foreign issues, political systems, and societal issues that are suggestive of hyperpartisanship in news titles.
  arXiv  Detail & Related papers  (2023-01-16T05:56:58Z)
• Design and analysis of tweet-based election models for the 2021 Mexican legislative election [55.41644538483948]
  We use a dataset of 15 million election-related tweets in the six months preceding election day. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods.
  arXiv  Detail & Related papers  (2023-01-02T12:40:05Z)
• A Spanish dataset for Targeted Sentiment Analysis of political headlines [0.0]
  This work addresses the task of Targeted Sentiment Analysis for the domain of news headlines, published by the main outlets during the 2019 Argentinean Presidential Elections. We present a polarity dataset of 1,976 headlines mentioning candidates in the 2019 elections at the target level. Preliminary experiments with state-of-the-art classification algorithms based on pre-trained linguistic models suggest that target information is helpful for this task.
  arXiv  Detail & Related papers  (2022-08-30T01:30:30Z)
• Predicting the 2020 US Presidential Election with Twitter [0.0]
  Electoral prediction utilizing social media data potentially would significantly affect campaign strategies. This paper explores past successful methods from research for analysis and prediction of the 2020 US Presidential Election using Twitter data. It is inconclusive whether this is an accurate method for predicting elections due to scarcity of data.
  arXiv  Detail & Related papers  (2021-07-19T14:59:25Z)
• Electoral Forecasting Using a Novel Temporal Attenuation Model: Predicting the US Presidential Elections [91.3755431537592]
  We develop a novel macro-scale temporal attenuation (TA) model, which uses pre-election poll data to improve forecasting accuracy. Our hypothesis is that the timing of publicizing opinion polls plays a significant role in how opinion oscillates, especially right before elections. We present two different implementations of the TA model, which accumulate an average forecasting error of 2.8-3.28 points over the 48-year period.
  arXiv  Detail & Related papers  (2020-04-30T09:21:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.