News Sentiment with ML/AI – Part 3

This is part 3 on evaluating news sentiment using ML/AI. Here is part 1 and part 2.

Recap

Previously on part 2 of this news analysis series, Google Cloud Natural Language was able to detect the emotional level and its sentiment. We looked at a CNBC article reviewing the iPhone and it is an emotional piece.

Let’s look at more articles and its analyses.

CNBC With Higest Positive Score

The CNBC news article with the highest positive score is titled: Victoria Beckham on juggling a fashion brand with family life: ‘I just do the best I can’.

It has a score of 0.4 with a magnitude of 6.4. The average score for all CNBC articles are -0.051443570457 with a magnitude of 5.6818897663138.

The article’s tone is positive as it presents hope for working women with a family. Here are some quotes:

When you’re a working mum, you feel torn, you feel guilty, but I just do the best that I can do. My kids and (soccer star husband) David will always come first.

that’s why we need to support each other, first and foremost.”

“I’m doing the best I can creatively, as a wife, as a mum

CNBC With Lowest Positive Score

The CNBC news article with the lowest score is titled: Mattis relationship with Trump reportedly frays as a decision on his fate looms, but White House dismisses.

It has a score of -0.699999988079071 with a magnitude of 2.0999999046325684. The average score for all CNBC articles are -0.051443570457 with a magnitude of 5.6818897663138.

The score is negative which means that is correlated to negative emotion. The article describes President Trump’s soured relationship with Secretary of Defense James Mattis. Here are are some quotes:

The relationship between President Donald Trump and Secretary of Defense James Mattis may have “soured” to the point of no return

Trump is … resentful of unflattering comparisons between the two men, the publication reported.

…the president is reportedly looking to replace the four star general…

NYTimes With Highest Positive Score

This NYTimes article has the highest score: 20 Wines Under $20: When Any Night Can Be a Weeknight.

It has a score of 0.4 with a whopping magnitude of 63.70000076293945. The average score for all NYTimes articles are -0.03707533304 with a magnitude of 18.0847858237.

The article is slightly positive but does have a high magnitude of 63.7. After reading through parts of the article, it is written with expressive and descriptive words. Here are some quotes:

Greatness in a wine is not solely a measure of complexity or profundity.

…represents a people and a culture and a love of wine, then a few extra dollars is a worthwhile investment.

But good ones, like this wine from La Staffa, grown in the Castelli di Jesi region in the northern Marche near the Adriatic, reawaken curiosity.

NYTimes With Lowest Score

This NYTimes article has the lowest score: Myanmar’s ‘Gravest Crimes’ Against Rohingya Demand Action, U.N. Says.

It has a score of -0.5 with a magnitude of 10.69999980926514. The average score for all NYTimes articles are -0.03707533304 with a magnitude of 18.0847858237.

The article is slightly negative as it details Myanmar army’s crime against a muslim minority group in Rakhine. It is written in a somber tone about a grave injustice. Here are some quotes:

…“the gravest crimes under international law”…

…troops shot some of the children and snatched infants from their mothers, throwing some into the river to drown while tossing others onto a fire, …

“The killing of civilians of all ages, including babies, cannot be argued to be a counterterrorism measure…“

Conclusion

After looking at both articles from CNBC and NYTimes, I am further impressed by Google’s ability to determine human expression. CNBC has an average magnitude of 5.68 compared to NYTimes 18.08. CNBC uses more common words whereas NYTimes’ articles are written with expressive and descriptive words. It is fascinating that an algorithm can make that distinction.

Articles from both sites are written fairly neutral. Depending on the content, some are slightly positive while others are slightly negative. I do not see exaggeration from both sides.

Analyzing news sources with Amazon AI/ML

I will be analyzing news sources with Amazon AI/ML and how positive or negative they are.
Why did I decided to explore this?
The are a few reasons.
One, I wanted more positivity in my life.
Two, I believe there are still good things happening around the world today.
Three, AI/ML is interesting and I wanted to learn more about it and use it to get positive results!
I started looking at the top news stories from CNBC, Buzzfeed, and NYTimes.
I read CNBC because I am interested in economics and how our financial markets are doing.
I included Buzzfeed and NYTimes because according to Wibbitz blog, Buzzfeed and NYTimes are the two most visited sites by millennials. [1]
I architected a system that automatically fetches the top news from each of these sites, scrape the content, and ran it through Amazon’s sentiment analysis.
Amazon sentiment analysis is a web service that uses ML/AI to determine the sentiment of some texts.
You send it some text and they tell you the positive, negative, neutral, and mixed score.
It also returns an overall emotion state of either positive, negative, or neutral.
An example they give is to use sentiment analysis is to use it on comments of a blog to determine if your readers liked the post. [2]
So far I have analyzed 1,030 articles and here are the results:
CNBC have a positive score of .025, a negative score of .039, a neutral score of .92, and a mixed score of .014.
NYTimes have a positive score of .055, a negative score of .044, a neutral score of .87, and a mixed score of .022.
I have the results for buzzfeed but after  reviewing the values, my code was not parsing all the pages in buzzfeed correctly so the results may be incorrect. I will be working on it and get an update soon.
So, what does this mean?
The Amazon AI sentiment analysis tells you the probability that something is either positive, negative, neutral or mixed.
Supposedly, you can take these numbers and convert it into a probability that something is positive.
I have asked on the Amazon forums and got an answer from a rep at Sep 15,2018 that the example is outdated.
However, my instinct is that each number represents the percentage of each sentiment.
For example, when you add all the positive, negative, neutral, and mixed score, you get 1.
Therefore, each of the number of a probability percentage of it being that sentiment.
In this case, CNBC has a 92% probability of being neutral and NYTimes has 87%.
NYTimes positive score is 5.5% vs CNBC 2.5%.
This may sound good but is it better for a news article to be neutral or positive?
These are questions that I love to explore.
Since I combine all the articles into a single number, I do not know which articles are deemed positive or negative.
Next time, I will break it down per article and let’s see how accurate the sentiment analysis is.

Demystifying Data Science Post

I read Demystifying Data Science For All from business over broadway. For someone new and interested in the space, this article is great.

Generally speaking, data science is a way of extracting value and insights from data using the powers of computer science and statistics applied to a specific field of study – Business over Broadway

I studied Biomedical Engineering and I work with data for my research. What makes  data scientist different is the increased size of the data set and the variable structure of that data. Today, we have data from servers, logs, mobile, IoT, 3rd party integrations such as salesforce, chat, Zendesk, etc. To complicate matters, some of the data are not structured.

I’m still not sure how you can analyze data that are not structured. The data I have worked with have all been structured. Sometimes, I need to transform that data so it can be easily analyzed.

The sheer volume of recorded data is called Big Data since there is a lot of it; hence, Big. Better technology enables us to store and process Big Data. Businesses want to understand and analyze this massive data to gain competitive advantage.

At my work, security is also another factor. Massive data is collected and security measures have to be taken so analysis does not reveal sensitive information. The article did not mention security but it should be taken seriously in any organization working with sensitive data.

I am surprised to see IBM listed as the leader in Data Science Platforms. Living in SF, I thought it would be Google or Amazon. They have the expertise in house so they should be able to monetize that expertise. Perhaps it is more valuable to use that expertise on their core businesses than to provide services to other companies.

My interests in Big Data is automation. How can someone take all this data and generate something useful with the least amount of resources. That’s where technology and computer science comes in.