From ae24d9a8869c497537839f330384cbadb2cf687c Mon Sep 17 00:00:00 2001 From: Mitja Felicijan Date: Tue, 31 Oct 2023 10:17:43 +0100 Subject: Updated theme --- ...timent-analysis-for-clickbait-detection-in-rss-feeds.html | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) (limited to 'public/using-sentiment-analysis-for-clickbait-detection-in-rss-feeds.html') diff --git a/public/using-sentiment-analysis-for-clickbait-detection-in-rss-feeds.html b/public/using-sentiment-analysis-for-clickbait-detection-in-rss-feeds.html index 77cc1cf..86d8f4d 100755 --- a/public/using-sentiment-analysis-for-clickbait-detection-in-rss-feeds.html +++ b/public/using-sentiment-analysis-for-clickbait-detection-in-rss-feeds.html @@ -49,12 +49,12 @@ article.

Results and assets

  1. Because of the small sample size further conclusions are impossible to make.
  2. Rule-based approach may not be the best way of doing this. By using deep learning we would be able to get better insights.
  3. Next step would be to periodically fetch RSS items and store them over a longer period of time and then perform analysis again and use either machine -learning or deep learning on top of it.
Relationship between title and description

Figure above displays difference between title and description sentiment for -specific RSS feed item. 1 means positive and -1 means negative sentiment.

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Going further


Posts from blogs I follow around the net