Analyzing the sentiment correlation between regular tweets and retweets

Chen, Jundong; Hossain, Shafaeat; Zhang, Huan

doi:10.1007/s13278-020-0624-4

Cited by 23 publications

(12 citation statements)

References 23 publications

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“…Specifically, metadata are used to select only Tweets written in English. Re-tweets are not distinguished from ordinary Tweets, under the premise that a user who re-Tweets is expressing a form of endorsement [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

“…The tools used in sentiment analysis are statistical techniques that explore and 0123456789(). : V,-vol extract emotions conveyed by selected texts [34][35][36][37][38][39], in terms of a discrete classification or a continuous score. Twitter data on COVID-19 pandemic has been used to study reactions to the outbreak in different countries [40][41][42], benchmark and validate new models for natural language processing [43][44][45], perform sentiment analysis about the pandemic [46][47][48], and conduct analyses about a specific event [49].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of lockdown perception in the United States during the COVID-19 pandemic

Surano

Porfiri

Rizzo

2021

Eur. Phys. J. Spec. Top.

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Containment measures have been applied throughout the world to halt the COVID-19 pandemic. In the United States, several forms of lockdown have been adopted in different parts of the country, leading to heterogeneous epidemiological, social, and economic effects. Here, we present a spatio-temporal analysis of a Twitter dataset comprising 1.3 million geo-localized Tweets about lockdown, from January to May 2020. Through sentiment analysis, we classified Tweets as expressing positive or negative emotions about lockdown, demonstrating a change in perception during the course of the pandemic modulated by socio-economic factors. A transfer entropy analysis of the time series of Tweets unveiled that the emotions in different parts of the country did not evolve independently. Rather, they were mediated by spatial interactions, which were also related to socio-ecomomic factors and, arguably, to political orientations. This study constitutes a first, necessary step toward isolating the mechanisms underlying the acceptance of public health interventions from highly resolved online datasets.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of lockdown perception in the United States during the COVID-19 pandemic

Surano

Porfiri

Rizzo

2021

Eur. Phys. J. Spec. Top.

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“…In machine learning techniques, a sentiment analysis model is built based on a training process populated with data labeled by humans (Messias et al 2017 ). This study uses the Lexicon-based approach, which is considered very effective for sentiment analysis (Chen et al 2020a , b ). After exploring the various tools, it was decided to go ahead with nltk.sentiment.vader sentiment intensity analyzer module available from Python.…”

Section: Methodsmentioning

confidence: 99%

Identification of affective valence of Twitter generated sentiments during the COVID-19 outbreak

Mittal

Aggarwal³

2021

Soc. Netw. Anal. Min.

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“…This is due to the fact that the relations in Twitter are established in a directed way between user and follower. In this area we can find the paper [25], in which the authors by means of network measures such as betweenness centrality and applying machine learning techniques, obtain correlations between the sentiments in retweets and regular tweets. According to the factor of the network metrics in this field, papers such as [26] and [25] predominate.These papers try to find relevant users in the social network by analysing their publications and connections.…”

Section: B Sentiment Analysis In Social Media Based On Network Metricsmentioning

confidence: 99%

“…In this area we can find the paper [25], in which the authors by means of network measures such as betweenness centrality and applying machine learning techniques, obtain correlations between the sentiments in retweets and regular tweets. According to the factor of the network metrics in this field, papers such as [26] and [25] predominate.These papers try to find relevant users in the social network by analysing their publications and connections. This is a very interesting research path, which deals with the relation of textual entities and network topology.…”

Section: B Sentiment Analysis In Social Media Based On Network Metricsmentioning

confidence: 99%

Non-Query-Based Pattern Mining and Sentiment Analysis for Massive Microblogging Online Texts

2020

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Pattern mining has been widely studied in the last decade given its great interest for research and its numerous applications in the real world. In this paper the definition of query and non-query based systems is proposed, highlighting the needs of non-query based systems in the era of Big Data. For this, we propose a new approach of a non-query based system that combines association rules, generalized rules and sentiment analysis in order to catalogue and discover opinion patterns in the social network Twitter. Association rules have been previously applied for sentiment analysis, but in most cases, they are used once the process of sentiment analysis is finished to see which tokens appear commonly related to a certain sentiment. On the other hand, they have also been used to discover patterns between sentiments. Our work differs from these in that it proposes a non-query based system which combines both techniques, in a mixed proposal of sentiment analysis and association rules to discover patterns and sentiment patterns in microblogging texts. The obtained rules generalize and summarize the sentiments obtained from a group of tweets about any character, brand or product mentioned in them. To study the performance of the proposed system, an initial set of 1.7 million tweets have been employed to analyse the most salient sentiments during the American pre-election campaign. The analysis of the obtained results supports the capability of the system of obtaining association rules and patterns with great descriptive value in this use case. Parallelisms can be established in these patterns that match perfectly with real life events.

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Analyzing the sentiment correlation between regular tweets and retweets

Cited by 23 publications

References 23 publications

Analysis of lockdown perception in the United States during the COVID-19 pandemic

Analysis of lockdown perception in the United States during the COVID-19 pandemic

Identification of affective valence of Twitter generated sentiments during the COVID-19 outbreak

Non-Query-Based Pattern Mining and Sentiment Analysis for Massive Microblogging Online Texts

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