Massive update to posts, archetypes

Added a archetypes for creating notes and posts so it auto-populates
fields.

Fixed existing posts so they align with the rule of 80 columns now.

1 files changed, 32 insertions, 11 deletions

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 30b0fd4..995da25 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
@@ -1,21 +1,29 @@
 ---
 title: Using sentiment analysis for clickbait detection in RSS feeds
 url: using-sentiment-analysis-for-clickbait-detection-in-rss-feeds.html
-date: 2019-10-19
+date: 2019-10-19T12:00:00+02:00
 draft: false
 ---
 
 ## 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.
+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.
+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.
+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.
+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:
 
@@ -39,9 +47,15 @@ for item in feed.entries:
 
 ## Perform sentiment analysis
 
-Since we now have cleaned up data in our `feed.entries` object we can start with performing 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.
+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
@@ -54,7 +68,9 @@ for item in feed.entries:
     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.
+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
@@ -69,12 +85,16 @@ 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.
+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.
+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](/assets/sentiment-analysis/sentiment-analysis.ipynb)
 
@@ -84,3 +104,4 @@ Figure above displays difference between title and description sentiment for spe
 - [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)
+