COVID-19-Outbreak-Tweets-Spain-es (COTwes)
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Discovering topics in Twitter about the COVID-19 outbreak in Spain---In this work, we apply topic modeling to study what users have been discussing in Twitter during the beginning of the COVID-19 pandemic. More particularly, we explore the period of time that includes three diﬀerentiated phases of the COVID-19 crisis in Spain: the pre-crisis time, the outbreak, and the beginning of the lockdown. To do so, we ﬁrst collect a large corpus of Spanish tweets and clean them. Then, we cluster the tweets into topics using a Latent Dirichlet Allocation model, and deﬁne generative and discriminative routes to later extract the most relevant keywords and sentences for each topic. Finally, we provide an exhaustive qualitative analysis about how such topics correspond to the situation in Spain at diﬀerent stages of the crisis' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["COVID-19","social media","Spanish","topic modeling","Twitter"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Discovering topics in Twitter about the COVID-19 outbreak in Spain---In this work, we apply topic modeling to study what users have been discussing in Twitter during the beginning of the COVID-19 pandemic. More particularly, we explore the period of time that includes three diﬀerentiated phases of the COVID-19 crisis in Spain: the pre-crisis time, the outbreak, and the beginning of the lockdown. To do so, we ﬁrst collect a large corpus of Spanish tweets and clean them. Then, we cluster the tweets into topics using a Latent Dirichlet Allocation model, and deﬁne generative and discriminative routes to later extract the most relevant keywords and sentences for each topic. Finally, we provide an exhaustive qualitative analysis about how such topics correspond to the situation in Spain at diﬀerent stages of the crisis' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Discovering topics in Twitter about the COVID-19 outbreak in Spain---In this work, we apply topic modeling to study what users have been discussing in Twitter during the beginning of the COVID-19 pandemic. More particularly, we explore the period of time that includes three diﬀerentiated phases of the COVID-19 crisis in Spain: the pre-crisis time, the outbreak, and the beginning of the lockdown. To do so, we ﬁrst collect a large corpus of Spanish tweets and clean them. Then, we cluster the tweets into topics using a Latent Dirichlet Allocation model, and deﬁne generative and discriminative routes to later extract the most relevant keywords and sentences for each topic. Finally, we provide an exhaustive qualitative analysis about how such topics correspond to the situation in Spain at diﬀerent stages of the crisis
COVID-19; social media; Spanish; topic modeling; Twitter