Temporal Analysis of Sentiment in Tweets: A Case Study with FIFA Confederations Cup in Brazil

Alves, André Luiz Firmino; Baptista, Cláudio de Souza; Firmino, Anderson Almeida; Oliveira, Maxwell Guimarães de; Figueirêdo, Hugo Feitosa de

doi:10.1007/978-3-319-10073-9_7

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Temporal analyses of sentiments expressed in Twitter data have been previously done on a variety of topics, including the FIFA Confederations Cup in Brazil (Alves et al, 2014), the changes in voters' feelings during the US elections (Paul et al, 2017) and the changes of sentiments on a monthly, daily and hourly level across different geographical regions (Hu et al, 2019).…”

Section: Sentiment Polaritymentioning

confidence: 99%

Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time

Wicke

Bolognesi

2021

Front. Commun.

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The words we use to talk about the current epidemiological crisis on social media can inform us on how we are conceptualizing the pandemic and how we are reacting to its development. This paper provides an extensive explorative analysis of how the discourse about Covid-19 reported on Twitter changes through time, focusing on the first wave of this pandemic. Based on an extensive corpus of tweets (produced between 20th March and 1st July 2020) first we show how the topics associated with the development of the pandemic changed through time, using topic modeling. Second, we show how the sentiment polarity of the language used in the tweets changed from a relatively positive valence during the first lockdown, toward a more negative valence in correspondence with the reopening. Third we show how the average subjectivity of the tweets increased linearly and fourth, how the popular and frequently used figurative frame of WAR changed when real riots and fights entered the discourse.

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Section: Sentiment Polaritymentioning

confidence: 99%

Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time

Wicke

Bolognesi

2021

Front. Commun.

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“…Most users share their personal thoughts and information all the time these days, which has led to a rise in the amount of information shared through social media. This information is a great source for an analyst or researcher seeking vital information for decision-making [11]. Since the beginning of the 21 st century, according to [11], sentiment analysis has been one of the most intriguing and active research subjects in the field of natural language processing.…”

Section: Review Of Literaturementioning

confidence: 99%

“…This information is a great source for an analyst or researcher seeking vital information for decision-making [11]. Since the beginning of the 21 st century, according to [11], sentiment analysis has been one of the most intriguing and active research subjects in the field of natural language processing. The act of analyzing a person's attitude or feeling based on their written words is known as sentiment analysis [12].…”

Section: Review Of Literaturementioning

confidence: 99%

Sentiment analysis of Twitter data regarding the agnipath scheme of the defense forces

Sajwan

Awasthi

Goel

et al. 2023

IJEECS

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Due to the popularity of social media today, people frequently share such criticism on Facebook, Twitter, Instagram, and other platforms. Therefore needs to know how your input from users of social media is generated in order to ascertain the public reaction to the policy that has been enacted. However, because of the comments, it is challenging to tell how many people have responded positive or negative. The objective of sentiment analysis of tweets is to provide insight into people’s attitudes and perceptions regarding an event. This study illustrates the role of Twitter in the announcement of a new army vacancy through the “agnipath scheme” dubbed “agniveer”. The result of this study can be used by the defense forces and government for decision making or policies related to the agnipath scheme. The study studied 4,000 English-language Twitter posts from July 3, 2022 to July 9, 2022. Manual text analysis revealed seven basic groups of tweet sentiments. The tweets’ positive, negative, and neutral emotions were shown using orange data mining software, a powerful machine learning, data mining, and data visualization toolset. Result shows that agnipath scheme is mostly accepted by the people.

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“…Teknik berbasis machine learning [15] yang diusulkan untuk masalah analisis sentimen dapat dibagi menjadi dua kelompok: (1) traditional models dan (2) deep learning models. Traditional models mengacu pada teknik pembelajaran mesin klasik, seperti pengklasifikasi Naïve Bayes [16], Maximum Entropy Classifier [17], atau Support Vector Machine (SVM) [18]. Masukan untuk algoritma tersebut mencakup fitur leksikal, fitur berbasis leksikon sentimen, bagian ucapan, atau kata sifat dan kata keterangan.…”

Section: Analisis Sentimenunclassified

Analisis Sentimen Terhadap Kenaikan Harga Bahan Bakar Minyak Menggunakan Long Short-Term Memory

Ikhsan

2023

IJCSR

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Pada tahun 2022 terjadi kenaikan harga bahan bakar minyak dunia. Dengan naiknya harga bahan bakar minyak ini secara tidak langsung akan berpengaruh terhadap harga material, inflasi, dan biaya hidup. Kenaikan harga bahan bakar minyak menjadi kontroversi hingga terjadi demo di berbagai wilayah. Kebijakan dinaikan harga bahan bakar minyak ini mendapat berbagai respons dari pengguna Twitter. Penelitian ini bertujuan untuk mengetahui akurasi prediksi dan pola sentimen model LSTM (Long Short-Term Memory) dari opini pengguna Twitter terhadap topik kenaikan harga bahan bakar minyak pada bulan Juli 2022. Data yang digunakan adalah data berbahasa English dengan dua kelas sentimen yaitu negative dan positive. Model LSTM dilakukan percobaan sebanyak lima kali. Hasil penelitian ini menunjukan bahwa akurasi tertinggi terdapat pada percobaan ke-1 dan ke-3 yaitu 90% dan pola sentimen cenderung positif.

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Temporal Analysis of Sentiment in Tweets: A Case Study with FIFA Confederations Cup in Brazil

Cited by 7 publications

References 7 publications

Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time

Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time

Sentiment analysis of Twitter data regarding the agnipath scheme of the defense forces

Analisis Sentimen Terhadap Kenaikan Harga Bahan Bakar Minyak Menggunakan Long Short-Term Memory

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