Which news publications have the most positive headlines? (simple sentiment analysis with Textblob)


Inspiration/base dataset

Kaggle provides a great dataset containing news headlines for most major publications. I decided to run some simple sentiment analysis using Textblob, a Python library for processing textual data, that comes with some pre-trained sentiment classifiers. One could of course train their own model, and probably obtain more accurate results overall, but I wasn't able to quickly fine a clean dataset of news headlines tagged with sentiment. Textblob should work fine for comparing the publications relative to each other.

To start, from Kaggle we're given a table as follows:

author content date month publication title url year
0 Avi Selk Uber driver Keith Avila picked up a p... 2016-12-30 12.0 Washington Post An eavesdropping Uber driver saved his 16-year... https://web.archive.org/web/20161231004909/htt... 2016.0
1 Sarah Larimer Crews on Friday continued to search L... 2016-12-30 12.0 Washington Post Plane carrying six people returning from a Cav... https://web.archive.org/web/20161231004909/htt... 2016.0
2 Renae Merle When the Obama administration announced a... 2016-12-30 12.0 Washington Post After helping a fraction of homeowners expecte... https://web.archive.org/web/20161231004909/htt... 2016.0
3 Chelsea Harvey This story has been updated. A new law in... 2016-12-30 12.0 Washington Post Yes, this is real: Michigan just banned bannin... https://web.archive.org/web/20161231004909/htt... 2016.0
4 Christopher Ingraham The nation’s first recreational marijuana... 2016-12-29 12.0 Washington Post What happened in Washington state after voters... https://web.archive.org/web/20161231004909/htt... 2016.0

After poking around the n-counts by year, it looks like 2016-2017 are the only years with decent data >~2000 headlines across each publication. So, I filtered the dataset to these two years:

#years came in string.0 format, just filtering them as they are for now

years = ['2016.0','2017.0']

df = df.loc[df['year'].isin(years)]


Running our data through Textblob to determine news headline sentiment

Great, so now we have a pretty clean dataframe containing headlines by news publication. Next, I'll create a utility function to log the polarity (ranging from -1 as most negative to 1 as most positive) of each headline

#used to analyze sentiment w/ textblob, leaving the raw output

def analize_sentiment_raw(headline):

'''

Function to lassify the polarity of a tweet

using textblob.

'''

analysis = TextBlob(headline)

#polarity = negative (-1) to positive (1)

return analysis.sentiment.polarity


Next, we'll just feed the headlines into the function we created above:

df['SA'] = np.array([ analize_sentiment_raw(headline) for headline in df['title']])


We can now see the polarity score (again ranging from -1 to 1) reflected in our dataframe:
title publication author date year month url content SA publish_year
0 An eavesdropping Uber driver saved his 16-year... Washington Post Avi Selk 2016-12-30 2016.0 12.0 https://web.archive.org/web/20161231004909/htt... Uber driver Keith Avila picked up a p... 0.00 2016
1 Plane carrying six people returning from a Cav... Washington Post Sarah Larimer 2016-12-30 2016.0 12.0 https://web.archive.org/web/20161231004909/htt... Crews on Friday continued to search L... -0.40 2016
2 After helping a fraction of homeowners expecte... Washington Post Renae Merle 2016-12-30 2016.0 12.0 https://web.archive.org/web/20161231004909/htt... When the Obama administration announced a... -0.05 2016
3 Yes, this is real: Michigan just banned bannin... Washington Post Chelsea Harvey 2016-12-30 2016.0 12.0 https://web.archive.org/web/20161231004909/htt... This story has been updated. A new law in... 0.20 2016
4 What happened in Washington state after voters... Washington Post Christopher Ingraham 2016-12-29 2016.0 12.0 https://web.archive.org/web/20161231004909/htt... The nation’s first recreational marijuana... 0.00 2016

Pivoting the data to rank news publications by headline sentiment

Now that we have sentiment attached to each news headline as reflected in col 'SA' above, we can pivot to summarize the publications with the most positive/negative headlines:

#grab a slice of the original df with just publication and polarity score

df_clean = df[['SA', 'publication']]

#pivoting to grab the average sentiment by publication

df_clean2 = df_clean.groupby(['publication']).mean().reset_index()

#preview the dataframe, sorting by most positive

df_clean2.sort_values(['SA'],ascending=False).head(15)


Below, we can see that (according to Textblob's pre-trained classifier) Business Insider has the most positive headlines on average, with Breitbart coming in at the bottom with the most negative headlines.

publication SA
2 Business Insider 0.060214
12 Vox 0.051213
7 NPR 0.043666
11 Talking Points Memo 0.035564
6 Guardian 0.032476
3 Buzzfeed News 0.028023
0 Atlantic 0.026798
13 Washington Post 0.022752
10 Reuters 0.020006
9 New York Post 0.019992
5 Fox News 0.016228
4 CNN 0.015503
8 National Review 0.014110
1 Breitbart 0.009544

Next, lets check out the n-counts on these, and add a view that shows the % of articles that are positive (score >0), negative (score <0), and neutral (score = 0).

#split the SA score into positive/neutral/negative

df_clean['positive'] = np.where(df['SA'] > 0, 1, 0)

df_clean['neutral'] = np.where(df['SA'] == 0, 1, 0)

df_clean['negative'] = np.where(df['SA'] < 0, 1, 0)

df_clean2 = df_clean[['publication','positive','neutral','negative']]

df_clean3 = df_clean2.groupby(['publication']).sum().reset_index()

df_clean3.sort_values(['positive'],ascending=False).head(15)

publication positive neutral negative
0 Atlantic 1435 4781 963
1 Breitbart 5469 13536 4701
2 Business Insider 2585 2852 1320
3 Buzzfeed News 1354 2549 951
4 CNN 1591 5056 1279
5 Fox News 990 2493 839
6 Guardian 2366 4558 1700
7 NPR 3451 6359 2010
8 National Review 1203 4064 904
9 New York Post 4677 9196 3573
10 Reuters 2423 6600 1664
11 Talking Points Memo 670 1499 393
12 Vox 1730 2134 1014
13 Washington Post 3175 5555 2348

Great, we can now see the # of headlines Textblob identified as positive/neutral/negative. Lets add some %s into this df too:

#fields for positive/neutral/negative percents

df_clean3['positive_perc'] = \

df_clean3['positive']/(df_clean3['positive']+df_clean3['neutral']+df_clean3['negative'])

df_clean3['neutral_perc'] = \

df_clean3['neutral']/(df_clean3['positive']+df_clean3['neutral']+df_clean3['negative'])

df_clean3['negative_perc'] = \

df_clean3['negative']/(df_clean3['positive']+df_clean3['neutral']+df_clean3['negative'])

#sort and preview dataframe

df_clean3.sort_values(['positive_perc'],ascending=False).head(15)

publication positive neutral negative positive_perc neutral_perc negative_perc
2 Business Insider 2585 2852 1320 0.382566 0.422081 0.195353
12 Vox 1730 2134 1014 0.354654 0.437474 0.207872
7 NPR 3451 6359 2010 0.291963 0.537986 0.170051
13 Washington Post 3175 5555 2348 0.286604 0.501444 0.211952
3 Buzzfeed News 1354 2549 951 0.278945 0.525134 0.195921
6 Guardian 2366 4558 1700 0.274351 0.528525 0.197124
9 New York Post 4677 9196 3573 0.268084 0.527112 0.204803
11 Talking Points Memo 670 1499 393 0.261514 0.585090 0.153396
1 Breitbart 5469 13536 4701 0.230701 0.570995 0.198304
5 Fox News 990 2493 839 0.229061 0.576816 0.194123
10 Reuters 2423 6600 1664 0.226724 0.617573 0.155703
4 CNN 1591 5056 1279 0.200732 0.637901 0.161368
0 Atlantic 1435 4781 963 0.199889 0.665970 0.134141
8 National Review 1203 4064 904 0.194944 0.658564 0.146492

Visualizing news publication sentiment

Using plotly, making a quick bar chart to visualize the news publication sentiment shown in our pivoted data above:

#bar chart

x = df_clean2['publication']

y = df_clean2['SA']

data = [

go.Bar(

x=x,

y=y,

marker=dict(

color='#8bffc0f2',

line=dict(

color='#d8ffea96',

width=1.5

),

),

opacity=1

)

]

layout = go.Layout(

title='News publications with the most/least positive headlines',

xaxis=dict(

title='News publication'

),

yaxis=dict(

title='Sentiment (higher = more positive',

),

margin=go.Margin(

l=140,

r=90,

b=140,

t=60,

pad=4

),

)

fig = go.Figure(data=data, layout=layout)

py.iplot(fig, filename='news_pub_sentiment')


Voila! We were able to quickly use Textblob to classify and rank sentiment across hundreds of thousands of news headlines.

For those interested, the jupyter notebook with the complete code can be found in our github repository.



Ace your next data science interview

Get better at data science interviews by solving a few questions per week



Find a bug? Submit a suggested change on Github, or message me on Twitter.