Prediction and Analysis of Moroccan Elections Using Sentiment Analysis

Oussous, Ahmed; Boulouard, Zakaria; Zahra, Benjelloun Fatima

doi:10.1007/978-3-030-90618-4_30

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…Similarly, Oussous, Boulouard & Zahra (2022) proposed another Arabic sentiment analysis framework that forecasted the Moroccan general election 2021. The data was collected in Arabic from the Hespress website (a Moroccan news website).…”

Section: Methodsmentioning

confidence: 99%

On the frontiers of Twitter data and sentiment analysis in election prediction: a review

Alvi,

Ali,

Ahmed

et al. 2023

PeerJ Computer Science

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Election prediction using sentiment analysis is a rapidly growing field that utilizes natural language processing and machine learning techniques to predict the outcome of political elections by analyzing the sentiment of online conversations and news articles. Sentiment analysis, or opinion mining, involves using text analysis to identify and extract subjective information from text data sources. In the context of election prediction, sentiment analysis can be used to gauge public opinion and predict the likely winner of an election. Significant progress has been made in election prediction in the last two decades. Yet, it becomes easier to have its comprehensive view if it has been appropriately classified approach-wise, citation-wise, and technology-wise. The main objective of this article is to examine and consolidate the progress made in research about election prediction using Twitter data. The aim is to provide a comprehensive overview of the current state-of-the-art practices in this field while identifying potential avenues for further research and exploration.

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Section: Methodsmentioning

confidence: 99%

On the frontiers of Twitter data and sentiment analysis in election prediction: a review

Alvi,

Ali,

Ahmed

et al. 2023

PeerJ Computer Science

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“…Another metric that shows the opposite tendency is Logarithmic Loss (LogLoss). This metric is calculated by the formula (12)…”

Section: Multiclass Classification Metricsmentioning

confidence: 99%

“…The majority of published papers in reputable journals are devoted to sentiment analysis (SA) of user comments from the Twitter social network [9][10][11]. The research topic of many papers also covers the presidential elections in the USA [9,10] and other countries [11,12]. At the same time, the works studying and describing complex social analytics platforms, such as [13], are not fully presented.…”

Section: Introductionmentioning

confidence: 99%

On the development of an information system for monitoring user opinion and its role for the public

et al. 2022

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Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users’ opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem’s social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system’s study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people’s moods and attitudes to the Government’s policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood.

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“…In recent years, social media has emerged as a powerful tool for public discourse, particularly in the context of politics and elections [1]. As a result, sentiment analysis has become a crucial tool for understanding public opinion and sentiment during elections [2]. However, sentiment analysis of election-related tweets poses unique challenges due to the complex nature of political language and the nuances of social dynamics involved [3].…”

Section: Introductionmentioning

confidence: 99%

Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

Khan,

Zhang,

Boudjellal

et al. 2023

Computers, Materials &Amp; Continua

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Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics. In recent years, the rise of social media platforms (SMPs) has provided a rich source of data for analyzing public opinions, particularly in the context of election-related conversations. Nevertheless, sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used, including figurative expressions, sarcasm, and the spread of misinformation. To address these challenges, this paper proposes Election-focused Bidirectional Encoder Representations from Transformers (ElecBERT), a new model for sentiment analysis in the context of election-related tweets. Election-related tweets pose unique challenges for sentiment analysis due to their complex language, sarcasm, and misinformation. ElecBERT is based on the Bidirectional Encoder Representations from Transformers (BERT) language model and is fine-tuned on two datasets: Election-Related Sentiment-Annotated Tweets (ElecSent)-Multi-Languages, containing 5.31 million labeled tweets in multiple languages, and ElecSent-English, containing 4.75 million labeled tweets in English. The model outperforms other machine learning models such as Support Vector Machines (SVM), Naïve Bayes (NB), and eXtreme Gradient Boosting (XGBoost), with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages, and an accuracy of 0.9930 and F1-score of 0.9899 on ElecSent-English. The performance of different models was compared using the 2020 United States (US) Presidential Election as a case study. The ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet, with the ElecBERT-English model achieving a Mean Absolute Error (MAE) of 6.13. This paper presents a valuable contribution to sentiment analysis in the context of election-related tweets, with potential applications in political analysis, social media management, and policymaking.

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Prediction and Analysis of Moroccan Elections Using Sentiment Analysis

Cited by 3 publications

References 18 publications

On the frontiers of Twitter data and sentiment analysis in election prediction: a review

On the frontiers of Twitter data and sentiment analysis in election prediction: a review

On the development of an information system for monitoring user opinion and its role for the public

Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

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