Spatio-Temporal Trend Analysis of the Brazilian Elections Based on Twitter Data

Praciano, Bruno J. G.; Costa, João Paulo; Maranhão, João Paulo A.; Mendonça, Fábio Lúcio Lópes de; Sousa, Rafael T. de; Prettz, Juliano Barbosa

doi:10.1109/icdmw.2018.00192

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…Additionally, in [33], Scikit-learn, NLTK, and VADER were used to analyze 121,594 tweets over two days about a candidate with an SVM accuracy of 0.99. Furthermore, study [34] employed Textblob, OpLexicon (Portuguese sentiment lexicon), and Sentilex (Portuguese sentiment lexicon) to analyze 158,279 tweets over 16 days about a candidate with SVM accuracy of 0.93 and 0.98 for OpLexicon/Sentilex. Moreover, study [35] used Long short-term memory (LSTM) to analyze 3,896 tweets over approximately three months, examining election trends, party, and candidate sentiment analysis, yielding precision = 0.76, recall = 0.75, and F1-score = 0.74.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…In Praciano et al [34], a framework for space-time trend analysis of the Brazilian presidential elections based on Twitter data was proposed. Experimental results showed that the proposed framework was very effective at predicting election results, as well as providing the geolocation timestamp and tweet, with an accuracy close to 90% when the Support Vector Machine (SVM) algorithm was applied for sentiment classification.…”

Section: Sentiment Analysis In Text Classificationmentioning

confidence: 99%

“…This paper proposes functions analogous to those presented in [29,30,[32][33][34] to determine the sentiment expressed by users, correlating the results to the facts that occurred in a certain period focused on a context of interest. However, it differs from the aforementioned works due to its operation being executed in real time, besides considering other words of a tweet that may express a sentiment, even if they are not marked with a hashtag.…”

Section: Main Contribution Of This Workmentioning

confidence: 99%

“…Figure 2 represents through the visualization layer the total set of data collected by the environment. In Figure 2a, peaks of tweets and retweets that were posted on the Brazilian team match days (June 17th (31,957), June 22th (33,949), June 27th (35,204), July 2nd (34,968) and July 6th (25,506)) can be noticed, the most commented hashtag about the topic in the quarterfinals being detailed in Section 6.8, in Section 6.9, the identification of outliers (users with discrepant activities over a period of time), and in Section 6.10, the analysis of a botnet used to spread tweets. The biggest spike of published tweets and retweets was observed on July 7th (42,733), a day after the quarterfinals (July 6th), the match in which the Brazilian National Soccer Team was eliminated from the FIFA World Cup 2018.…”

Section: General Collection Summary Presentationmentioning

confidence: 99%

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Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis

Júnior

Albuquerque

Andrade

et al. 2020

Sensors

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Currently, social networks present information of great relevance to various government agencies and different types of companies, which need knowledge insights for their business strategies. From this point of view, an important technique for data analysis is to create and maintain an environment for collecting data and transforming them into intelligence information to enable analysts to observe the evolution of a given topic, elaborate the analysis hypothesis, identify botnets, and generate data to aid in the decision-making process. Focusing on collecting, analyzing, and supporting decision-making, this paper proposes an architecture designed to monitor and perform anonymous real-time searches in tweets to generate information allowing sentiment analysis on a given subject. Therefore, a technological structure and its implementation are defined, followed by processes for data collection and analysis. The results obtained indicate that the proposed solution provides a high capacity to collect, process, search, analyze, and view a large number of tweets in several languages, in real-time, with sentiment analysis capabilities, at a low cost of implementation and operation.

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“…Another line of work focuses on machine learning methods, as these can achieve high accuracy for predicting the results. For example, Praciano et al [23] analyzed the political trends prevailing in Brazil in 2014 before the presidential election through text classification and sentimental analysis from Twitter data. For analyzing the tweets tags, links and mentions were considered, as well as using geographic information for visualizing the results.…”

Section: Related Workmentioning

confidence: 99%

TwiFly: A Data Analysis Framework for Twitter

et al. 2020

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Over the last decade, there have been many changes in the field of political analysis at a global level. Through social networking platforms, millions of people have the opportunity to express their opinion and capture their thoughts at any time, leaving their digital footprint. As such, massive datasets are now available, which can be used by analysts to gain useful insights on the current political climate and identify political tendencies. In this paper, we present TwiFly, a framework built for analyzing Twitter data. TwiFly accepts a number of accounts to be monitored for a specific time-frame and visualizes in real time useful extracted information. As a proof of concept, we present the application of our platform to the most recent elections of Greece, gaining useful insights on the election results.

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Spatio-Temporal Trend Analysis of the Brazilian Elections Based on Twitter Data

Cited by 7 publications

References 19 publications

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

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

Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis

TwiFly: A Data Analysis Framework for Twitter

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