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Firazyr (Icatibant Injection for Subcutaneous Administration)- FDA

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We then convert the cleaned articles into a TF-IDF matrix, excluding the most common and rarest words. Finally, we assign training data and ground-truth labels using a topic-count matrix. We use the cleaned website text content, along with the topics, to train a neural network classifier that classifies the collected videos for news topics. Note that the contribution of this paper is not to present a novel method but rather to apply well-established machine learning methods to our research problem.

Additionally, we create a custom class to cross-validate and evaluate the FFNN, since Keras does not provide support for cross-validation by default.

The YouTube content is not tagged, only containing generic classes chosen when uploading the videos on YouTube.

From a technical point of view, this is a multilabel classification problem, as one news article is typically labeled for several topics. Note, however, that for statistical testing we only utilize the highest-ranking topic per a news story.

More specifically, the output of the FFNN classifier is a matrix of confidence values for the combination of each news story and each topic. This is done for parsimony, as using all or several topics per story would make the statistical comparison exceedingly complex. Here, we report the key evaluation methods and results of the topic classification. Note that a full evaluation study of the applied FFNN classifier is presented in Salminen et al.

First, to optimize the parameters of the FFNN model, we create a helper class to conduct random optimization on both the TF-IDF matrix creation and the FFNN parameters. Hope, we identify a combination of FFNN parameters Firazyr (Icatibant Injection for Subcutaneous Administration)- FDA the motion sick space that provides the highest F1 Score (i.

Therefore, we do not use LDA but rather train a supervised classifier based on manually annotated data by journalists that can be considered as experts of news topics.

We apply the model trained on website content (i. Intuitively, we presume this approach works because the news topics covered in the YouTube channel are highly similar to those published on the website (e. Because we lack ground truth (there are no labels in Firazyr (Icatibant Injection for Subcutaneous Administration)- FDA videos), we evaluate the validity of the machine-classified results by using three human coders to classify a sample of Firazyr (Icatibant Injection for Subcutaneous Administration)- FDA videos using the same taxonomy that the machine applied.

We then measure the simple agreement between the chosen topics by machine and human raters and find that the average agreement between the three human raters and the machine is 70. Considering the high number of classes, we are satisfied with this result. In terms of success rate, the model provided a label for 96.

This definition is relevant to our research, since it specifically focuses on online comments of which our dataset consists. Note that Perspective API is a publicly available service for toxicity prediction of social media comments, enabling replicability of the scoring process.

We utilize the Perspective API to score the comments collected for this study. After obtaining an access key to the API, we test its performance. The version of the API at the time of the study had two main types of models: (a) alpha Firazyr (Icatibant Injection for Subcutaneous Administration)- FDA and (b) experimental models. The alpha models include the default toxicity scoring model, while the experimental models include the severe toxicity, fast toxicity, attack on author, Firazyr (Icatibant Injection for Subcutaneous Administration)- FDA on commenter, incoherent (i.

According to the API documentation, failure to provide scores can be due to non-English content, and too long comments. Overall, we were able to successfully score 240,554 comments, representing 78. A manual inspection showed that Perspective API was able to detect the toxicity of the comments well.

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Comments:

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