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Figure 6 The top 3 probable acoustic words for the highest ranked emotion-specific topics obtained using RSM.
Topics obtained via LDA capture neutral emotions slightly better than RSM, whereas, the latter achieves better separability in case of the remaining emotions - sad, happy, and angry.
Between LDA and RSM, we can observe that the topics obtained via LDA capture neutral emotions slightly better than RSM, i.e. the highly ranked topics for neutral emotions co-occur less with other emotions.
The second phase involved four focus group sessions which explored topics obtained from the survey.
The second phase involved focus group sessions which explored in more detail topics obtained from the survey.
Although these topics obtained low scores in the rankings of survey respondents, they constitute essential areas that need to continue to be developed.
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This observation suggests that while the ratio is centered around 1.2 for the majority of topics, there are a few topics obtain large amount of repeated attention.
For two topics, obtaining direct patient interview data post-discharge was not feasible due to either the nature of the patient population or service delivery issues.
Then, this topic obtained large attention in transport engineering literature and several methods have been proposed for investigating its variability.
The data consists of participant's constructions of a given topic, obtained by ranking a set of pre-defined items according to his or her perspective and experience.
We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com