Sentiment Analysis with Hedonometer Sample Clauses

Sentiment Analysis with Hedonometer. Hedonometer is employed in NLP to gauge the scale of happiness conveyed by texts. Hedonometer computes the weighted average magnitude of joyful- ness by a frequency-based measurement [3]. Similar to Stanford CoreNLP, Hedonometer gives a score for the sentence’s scale of happiness, as well as categorizing it into positive, neutral, or negative. The Hedonometer measurement model is not context-dependent, but rather relies on a combination of texts collected from Google Books, New York Times, Lyrics, and Tweets to train the model to assign scores of hap- piness to new corpora, which may fail to yield an accuracy as high as that of Stanford CoreNLP. Nevertheless, when ▇▇▇▇▇ and his fellow researchers developed Hedonometer, they make this model highly efficient when applied to social media texts like blogs and tweets [3]. Therefore, Hedonometer is incorporated in this study to help assess the level of pleasure conveyed in Covid-themed tweets.
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Sentiment Analysis with Hedonometer. As shown in Figure 5.2, the change of frequency of both positive and negative tweets is much less striking compared with the output of Stanford CoreNLP. Also noticeably, both positive (fluctuating at 8%) and negative (fluctuating at 2%) tweets only make up a small proportion of the whole dataset, meaning that about 90% of tweets are categorized by Hedonometer as having neutral emotions, which is contradictory to the actual sentiment displayed by the collected tweets.
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