A Hybrid Method for Sentiment Analysis of Election Related Tweets

Beleveslis, Dimosthenis; Tjortjis, Christos; Psaradelis, Dimitris; Nikoglou, Dimitris

doi:10.1109/seeda-cecnsm.2019.8908289

Cited by 12 publications

(3 citation statements)

References 6 publications

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“…For instance, in a study [25], the authors employed tools such as Natural Language Toolkit (NLTK), Tweet Natural Language Processing (TweetNLP) toolkit, Scikit-learn, and Statistical Package for the Social Sciences (SPSS) statistical package to predict ideological orientation (conservative or rightleaning, progressive or left-leaning) of 24,900 tweets collected over 9 h during an election, achieving an overall accuracy of 99.8% using Random Forest. Similarly, in [26], Scikit-learn was utilized to analyze 46,705 Greek tweets over 20 days during an election, achieving a Random Forest accuracy of 0.80 and precision values of Negative = 0.74, Neutral = 0.83, and Positive = 1.…”

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

View full text Add to dashboard Cite

show abstract

“…As an example, terms such as wonderful, beautiful, and joy have a positive sentiment score, while terms such as fear and sadness have a negative one [90]. SA techniques are applied to almost every social domain because opinions are critical to almost all human behaviors [136]. Opinion mining and sentiment analysis methods can be applied to the SM comments [137] to automatically identify issues that concern citizens, as well as features they liked [138].…”

Section: Sentiment Analysismentioning

confidence: 99%

Data Mining Algorithms for Smart Cities: A Bibliometric Analysis

Kousis

Tjortjis

2021

Algorithms

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Smart cities connect people and places using innovative technologies such as Data Mining (DM), Machine Learning (ML), big data, and the Internet of Things (IoT). This paper presents a bibliometric analysis to provide a comprehensive overview of studies associated with DM technologies used in smart cities applications. The study aims to identify the main DM techniques used in the context of smart cities and how the research field of DM for smart cities evolves over time. We adopted both qualitative and quantitative methods to explore the topic. We used the Scopus database to find relative articles published in scientific journals. This study covers 197 articles published over the period from 2013 to 2021. For the bibliometric analysis, we used the Biliometrix library, developed in R. Our findings show that there is a wide range of DM technologies used in every layer of a smart city project. Several ML algorithms, supervised or unsupervised, are adopted for operating the instrumentation, middleware, and application layer. The bibliometric analysis shows that DM for smart cities is a fast-growing scientific field. Scientists from all over the world show a great interest in researching and collaborating on this interdisciplinary scientific field.

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“…Studies [8] and [9] both focused solely on the polarity dimension and utilized traditional/outdated Natural Language Processing (NLP) tools, combined with classic machine learning algorithms (e.g., random forest, decision trees, Support Vector Machine classifiers). In contrast, [10] focused on offensiveness and exploited DNNs, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks.…”

Section: Introductionmentioning

confidence: 99%

GreekPolitics: Sentiment Analysis on Greek Politically Charged Tweets

Patsiouras,

Koroni,

Mademlis

et al. 2023

2023 31st European Signal Processing Conference (EUSIPCO)

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The rapid growth of on-line social media platforms has rendered opinion mining/sentiment analysis a critical area of research. This paper focuses on analyzing Twitter posts (tweets), written in the Greek language and politically charged in content. This is a rather underexplored topic, due to the inadequacy of publicly available annotated datasets. Thus, we present and release GreekPolitics: a dataset of Greek tweets with politically charged content, annotated for four different sentiments: polarity, figurativeness, aggressiveness and bias. GreekPolitics has been evaluated comprehensively using state-of-the-art Deep Neural Networks (DNNs) and data augmentation methods. This paper details the dataset, the evaluation process and the experimental results.

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A Hybrid Method for Sentiment Analysis of Election Related Tweets

Cited by 12 publications

References 6 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

Data Mining Algorithms for Smart Cities: A Bibliometric Analysis

GreekPolitics: Sentiment Analysis on Greek Politically Charged Tweets

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