Posts published in the social media are a good source of feedback to assess the impact of advertising campaigns. Whereas most of the published corpora of messages in the Sentiment Analysis domain tag posts with polarity labels, this paper presents a corpus in Spanish language where tagging has been made using 8 predefined emotions: love-hate, happiness-sadness, trust-fear, satisfaction-dissatisfaction. In every post, extracted from Twitter, sentiments have been annotated towards each specific brand under study. The corpus is published as a collection of RDF resources with links to external entities. Also a vocabulary describing this emotion classification along with other relevant aspects of customer’s opinion is provided.
Posts published in the social media are a good source of feedback to assess the impact of advertising campaigns. Whereas most of the published corpora of messages in the Sentiment Analysis domain tag posts with polarity labels, this paper presents a corpus in Spanish language where tagging has been made using 8 predefined emotions: love-hate, happiness-sadness, trust-fear, satisfaction-dissatisfaction. In every post, extracted from Twitter, sentiments have been annotated towards each specific brand under study. The corpus is published as a collection of RDF resources with links to external entities. Also a vocabulary describing this emotion classification along with other relevant aspects of customer’s opinion is provided. Read More
Related posts:
Spanish corpora for sentiment analysis: a survey
India ‘to tighten laws on alcohol event advertising, “surrogate” ads’
Detecting Topics and Polarity From Twitter: A University Faculty Case
Analyzing the Emotions That News Agencies Express towards Candidates during Electoral Campaigns: 2018 Brazilian Presidential Election as a Case of Study
Virality, only the tip of the iceberg: ways of spread and interaction around COVID-19 misinformation in Twitter
Transformative advertising: well-being Instagram messaging