From 9f5454bda6299db43a4e9de5b3716471388b81d9 Mon Sep 17 00:00:00 2001 From: Mitja Felicijan Date: Sat, 27 Aug 2022 14:05:48 +0200 Subject: Move blog to Hugo --- ...g-sentiment-analysis-for-clickbait-detection.md | 88 ---------------------- 1 file changed, 88 deletions(-) delete mode 100644 posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md (limited to 'posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md') diff --git a/posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md b/posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md deleted file mode 100644 index 831b490..0000000 --- a/posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md +++ /dev/null @@ -1,88 +0,0 @@ ---- -Title: Using sentiment analysis for clickbait detection in RSS feeds -Description: Using Python with sentiment analysis to detect if titles in RSS feeds are clickbait -Slug: using-sentiment-analysis-for-clickbait-detection-in-rss-feeds -Listing: true -Created: 2019-10-19 -Tags: [] ---- - -## Initial thoughts - -One of the things that interested me for a while now is if major well established news sites use click bait titles to drive additional traffic to their sites and generate additional impressions. - -Goal is to see how article titles and actual content of article differ from each other and see if titles are clickbaited. - -## Preparing and cleaning data - -For this example I opted to just use RSS feed from a new website and decided to go with [The Guardian](https://www.theguardian.com) World news. While this gets us limited data (~40) articles and also description (actual content) is trimmed this really doesn't reflect the actual article contents. - -To get better content I could use web scraping and use RSS as link list and fetch contents directly from website, but for this simple example this will suffice. - -There are couple of requirements we need to install before we continue: - -- `pip3 install feedparser` (parses RSS feed from url) -- `pip3 install vaderSentiment` (does sentiment polarity analysis) -- `pip3 install matplotlib` (plots chart of results) - -So first we need to fetch RSS data and sanitize HTML content from description. - -```python -import re -import feedparser - -feed_url = "https://www.theguardian.com/world/rss" -feed = feedparser.parse(feed_url) - -# sanitize html -for item in feed.entries: - item.description = re.sub('<[^<]+?>', '', item.description) -``` - -## Perform sentiment analysis - -Since we now have cleaned up data in our `feed.entries` object we can start with performing sentiment analysis. - -There are many sentiment analysis libraries available that range from rule-based sentiment analysis up to machine learning supported analysis. To keep things simple I decided to use rule-based analysis library [vaderSentiment](https://github.com/cjhutto/vaderSentiment) from [C.J. Hutto](https://github.com/cjhutto). Really nice library and quite easy to use. - -```python -from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer -analyser = SentimentIntensityAnalyzer() - -sentiment_results = [] -for item in feed.entries: - sentiment_title = analyser.polarity_scores(item.title) - sentiment_description = analyser.polarity_scores(item.description) - sentiment_results.append([sentiment_title['compound'], sentiment_description['compound']]) -``` - -Now that we have this data in a shape that is compatible with matplotlib we can plot results to see the difference between title and description sentiment of an article. - -```python -import matplotlib.pyplot as plt - -plt.rcParams['figure.figsize'] = (15, 3) -plt.plot(sentiment_results, drawstyle='steps') -plt.title('Sentiment analysis relationship between title and description (Guardian World News)') -plt.legend(['title', 'description']) -plt.show() -``` - -## 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](/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. - -[ยป Download Jupyter Notebook](/assets/sentiment-analysis/sentiment-analysis.ipynb) - -## Going further - -- [Twitter Sentiment Analysis by Bryan Schwierzke](https://github.com/bswiss/news_mood) -- [AFINN-based sentiment analysis for Node.js by Andrew Sliwinski](https://github.com/thisandagain/sentiment) -- [Sentiment Analysis with LSTMs in Tensorflow by Adit Deshpande](https://github.com/adeshpande3/LSTM-Sentiment-Analysis) -- [Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc. by Abdul Fatir](https://github.com/abdulfatir/twitter-sentiment-analysis) -- cgit v1.2.3