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1<!doctype html><html lang=en-us><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=generator content="JBMAFP - github.com/mitjafelicijan/jbmafp"><link href="data:image/x-icon;base64,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" rel=icon type=image/x-icon><title>Using sentiment analysis for clickbait detection in RSS feeds</title><meta name=description content="Initial thoughtsOne of the things that interested me for a while now is if major wellestablished news sites use click bait titles to drive additional traffic totheir sites and generate additional impressions."><link rel=alternate type=application/rss+xml title="Mitja Felicijan's posts" href=https://mitjafelicijan.com/index.xml><link rel=alternate type=application/rss+xml title="Mitja Felicijan's notes" href=https://mitjafelicijan.com/notes.xml><style>:root{--border-color:gainsboro;--border-size:2px;--link-color:blue;--bg-color:#eee}body{padding:2.5rem;max-width:1900px;background:#fff;font-family:sans-serif;line-height:1.35rem;font-size:16px}hr{border:0;border-bottom:var(--border-size)solid var(--border-color);margin-block-start:1.5rem}a{color:var(--link-color);text-decoration:none}h1,h2,h3{line-height:initial}h1{font-size:xx-large}footer{margin-block-start:2rem}cap{text-transform:capitalize}blockquote{font-style:italic}table{max-width:100%;border:var(--border-size)solid var(--border-color);border-collapse:separate;border-spacing:0}table thead tr th{border-bottom:var(--border-size)solid var(--border-color);text-align:left}table th,table td{padding:.5em .8em}ul.list li{padding:.2em 0}ul{line-height:1.35em}pre{text-wrap:nowrap;overflow-x:auto;padding:0 1em;border:var(--border-size)solid var(--border-color)}code{padding:0 3px;font-size:14px;border:0;background:var(--bg-color)}pre code{line-height:1.3em;background:#fff}pre,code,pre *,code *{font-family:monospace}figure{margin-inline-start:0;margin-inline-end:0}figcaption{text-align:center}figcaption p{margin:.3em 0 0}img,video,audio{width:800px;max-width:100%}header nav{display:flex;gap:.9rem}audio::-webkit-media-controls-enclosure{border-radius:0}@media only screen and (max-width:600px){body{padding:.5em;word-wrap:break-word}header nav{gap:.7rem}header nav .hob{display:none}a{word-wrap:break-word}}</style><header><nav class=main itemscope itemtype=http://schema.org/SiteNavigationElement role=toolbar><a href=/>Home</a> 1<!doctype html><html lang=en-us><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=generator content="JBMAFP - 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2<a href=/#posts>Posts</a> 2<a href=/#posts>Posts</a>
3<a href=/#notes>Notes</a> 3<a href=/#notes>Notes</a>
4<a href=/#sideprojects class=hob>Side Projects</a> 4<a href=/#sideprojects class=hob>Side Projects</a>
@@ -49,7 +49,7 @@ article.<pre tabindex=0 style=background-color:#fff><code><span style=display:fl
49</span></span></code></pre><h2 id=results-and-assets>Results and assets</h2><ol><li>Because of the small sample size further conclusions are impossible to make.<li>Rule-based approach may not be the best way of doing this. By using deep 49</span></span></code></pre><h2 id=results-and-assets>Results and assets</h2><ol><li>Because of the small sample size further conclusions are impossible to make.<li>Rule-based approach may not be the best way of doing this. By using deep
50learning we would be able to get better insights.<li><strong>Next step would be to</strong> periodically fetch RSS items and store them over a 50learning we would be able to get better insights.<li><strong>Next step would be to</strong> periodically fetch RSS items and store them over a
51longer period of time and then perform analysis again and use either machine 51longer period of time and then perform analysis again and use either machine
52learning or deep learning on top of it.</ol><figure><img loading="lazy" src=/posts/sentiment-analysis/guardian-sa-title-desc-relationship.png alt="Relationship between title and description"></figure><p>Figure above displays difference between title and description sentiment for 52learning or deep learning on top of it.</ol><figure><img src=/posts/sentiment-analysis/guardian-sa-title-desc-relationship.png alt="Relationship between title and description"></figure><p>Figure above displays difference between title and description sentiment for
53specific RSS feed item. 1 means positive and -1 means negative sentiment.<p><a href=/posts/sentiment-analysis/sentiment-analysis.ipynb>» Download Jupyter Notebook</a><h2 id=going-further>Going further</h2><ul><li><a href=https://github.com/bswiss/news_mood>Twitter Sentiment Analysis by Bryan Schwierzke</a><li><a href=https://github.com/thisandagain/sentiment>AFINN-based sentiment analysis for Node.js by Andrew Sliwinski</a><li><a href=https://github.com/adeshpande3/LSTM-Sentiment-Analysis>Sentiment Analysis with LSTMs in Tensorflow by Adit Deshpande</a><li><a href=https://github.com/abdulfatir/twitter-sentiment-analysis>Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc. by Abdul Fatir</a></ul></div></article></main><section><hr><h2>Posts from blogs I follow around the net</h2><ul><li><a href=https://utcc.utoronto.ca/~cks/space/blog/linux/NFSv4ServerLockClients target=_blank rel=noopener>Finding which NFSv4 client owns a lock on a Linux NFS(v4) server</a> — <a href=https://utcc.utoronto.ca/~cks/space/blog/>Chris's Wiki :: blog</a><div>A while back I wrote an entry about finding which NFS client owns 53specific RSS feed item. 1 means positive and -1 means negative sentiment.<p><a href=/posts/sentiment-analysis/sentiment-analysis.ipynb>» Download Jupyter Notebook</a><h2 id=going-further>Going further</h2><ul><li><a href=https://github.com/bswiss/news_mood>Twitter Sentiment Analysis by Bryan Schwierzke</a><li><a href=https://github.com/thisandagain/sentiment>AFINN-based sentiment analysis for Node.js by Andrew Sliwinski</a><li><a href=https://github.com/adeshpande3/LSTM-Sentiment-Analysis>Sentiment Analysis with LSTMs in Tensorflow by Adit Deshpande</a><li><a href=https://github.com/abdulfatir/twitter-sentiment-analysis>Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc. by Abdul Fatir</a></ul></div></article></main><section><hr><h2>Posts from blogs I follow around the net</h2><ul><li><a href=https://utcc.utoronto.ca/~cks/space/blog/linux/NFSv4ServerLockClients target=_blank rel=noopener>Finding which NFSv4 client owns a lock on a Linux NFS(v4) server</a> — <a href=https://utcc.utoronto.ca/~cks/space/blog/>Chris's Wiki :: blog</a><div>A while back I wrote an entry about finding which NFS client owns
54a lock on a Linux NFS server, which turned 54a lock on a Linux NFS server, which turned
55out to be specific to NFS v3 (which I really should have seen coming, 55out to be specific to NFS v3 (which I really should have seen coming,