From 70baaeb4e78d12c329a03e929fd30d41730ed2b1 Mon Sep 17 00:00:00 2001 From: Mitja Felicijan Date: Sun, 28 Aug 2022 05:39:44 +0200 Subject: Moved statis assets and converted all CSS to Tailwind --- .../2019-10-19-using-sentiment-analysis-for-clickbait-detection.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'content/posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md') diff --git a/content/posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md b/content/posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md index 0f5d994..30b0fd4 100644 --- a/content/posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md +++ b/content/posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md @@ -72,11 +72,11 @@ plt.show() 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](/sentiment-analysis/guardian-sa-title-desc-relationship.png) +![Relationship between title and description](/assets/sentiment-analysis/guardian-sa-title-desc-relationship.png) Figure above displays difference between title and description sentiment for specific RSS feed item. 1 means positive and -1 means negative sentiment. -[» Download Jupyter Notebook](/sentiment-analysis/sentiment-analysis.ipynb) +[» Download Jupyter Notebook](/assets/sentiment-analysis/sentiment-analysis.ipynb) ## Going further -- cgit v1.2.3