Entity Analysis with ML/AI – Part 4

This is part 4 on analyzing news articles using ML/AI. Here is part 1, part 2, and part 3.

Recap

Previously on part 3 of this series, Google Cloud Natural Language detects expressive articles and assigns it with a higher magnitude with most of the news articles having a neutral tone.

In this article, I explore their entity detection service.

Entity Analysis

Google describes its entity analysis as such:

Entity Analysis inspects the given text for known entities (proper nouns such as public figures, landmarks, etc.), and returns information about those entities.

Gcloud entity returns a list of entities, types (i.e. PERSON) and its salience score. Salience score is a number between 0 to 1 describing how important or central the entity is. Read more from the official documentation.

Entity analyses returns many entities per article so I filter it to those with salience score greater than 0.1. I am only interested in entities that have significant context in the artcles.

Entity Analysis Article #1

I pick a news article that caught my eye, titled How Chicago Is Changing Theater, One Storefront at a Time. I have recently visited the beautiful city of Chicago but didn’t get a chance to watch any shows.

One entity that Google returns is Red Tape Theater with a category of ORGANIZATION and a salience score of 0.1361. This entity barely passes the 0.1 threshold. Google gave me some metadata and suggested a wikipedia link to Red Tape which is an article for excessive bureaucratic regulation. Google is wrong on that one. It cannot differentiate between the Red Tape Theater and Red Tape as an idiom.

Other entities are mentioned in this article such as WildClaw Theater, The Den, Firebrand Theater, Broken Nose Theater, First Floor Theater, and many more. Since most of those entities are briefly mentioned, the salience score is probably < 0.1. Considering how Red Tape Theater has a salience score of only 0.1361, it seems that not a single theater is central to this article. Instead, there are many theaters presented but Red Tape Theater is the most prominent one. That is evident when I read the article.

Towards the end, Red Tape Theater is mentioned and the next several paragraphs are dedicated to it. If I were to pick a theater to visit, I would go with the Red Tape Theater and see The Shipment. The second most prominent might be the Steppenwolf Theater Company but only one paragraph is dedicated to it.

Conclusion

This is another impressive feat by Google’s machine learning. It is able to detect the most prominent entity in the article. It knows this by relating several paragraphs to the Red Tape Theater.

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.

Article Sentiment Through AI

This is part 1 on evaluating sentiments using ML/AI of news articles.

This post builds on work from last week as I explore news articles with ML/AI. To recap, I aggregated the top news from CNBC and NYTimes and calculated their overall sentiment score. However, since all the news articles are combined together, there is no way to evaluate them individually.

In this post, I will examine the individual article’s sentiment.

Methodology

Last week I use AWS Comprehend; however, this week I will using Google Cloud Natural Language.

Why the change?

Because of AWS’s limitations. According to their guidelines and limits, the maximum size for sentiment detection is 5KB. That is a mere 2,500 words!If an article goes over 2500 words, I have to split them, and I have to analysis separately. Then, I need to weigh them appropriately and did a final calculation. I am lazy so I seek a better solution. I found it with Google Cloud Natural Language

Note to businesses: This is a reason why customers switch to a different service.

Google Cloud Natural Language

Google Cloud Natural Language derive insights from unstructured text using Google maching learning

Google’s sentiment analysis is less specific than AWS. Google provides two values: magnitude and score. A score of 0 is neutral. A score of less than 0 is considered to have negative emotion and a score that is greater than 0 is considered to have positive emotion. The magnitude indicates the level of emotional content. Pretty vague but let’s take a look at some samples. You can read more about Google’s sentiment analysis here.

Results

Enough theory! Let’s analyze some examples of the output and see if they make sense.

Here is the results for the average magnitude and score for CNBC and NYTimes:

source avg(gcloud_magnitude) avg(gcloud_score)
cnbc 4.656716405678151260447761194030 -0.082835822463480393880597014925
nytimes 14.884188043510812147863247863248 -0.060256411440861528376068376068

NYTimes is more emotional based on its average gcloud_magnitude score, 4.65 vs 14.88. The sentiment score for both is very close to 0 so they are both neither positive or negative.

From last article’s analysis, CNBC has a 92% and NYTimes has a 87% probability of being neutral respectively. Both AWS and Google seems to agree that the sentiments are most likely neutral.

Individual Article Analysis

Here is the focus point of this article, let’s evaluate one of the articles.

CNBC With Higest Magnitude
url gcloud_magnitude gcloud_score
https://www.cnbc.com/2018/09/18/iphone-xs-and-iphone-xs-max-review.html 30.3999996185302700 0.2000000029802322

This CNBC article reviews Apple’s new iPhones and it generates a magnitude score of 30.39, much higher than the average score of 4.65 for CNBC.

I read the article and there are phrases that indicates how emotional dramatic the article is written in. Here are some quotes:

They’re the best phones Apple has ever made.

The iPhone X, even a year later, is still arguably the best phone on the market.

It’s one of the best screens on the market…

The speakers sound awesome.

I love how shiny it is on the new gold and white models.

I love that iOS 12 gives you so much more control over notifications.

…these are the best phones Apple has made…

Judging from some of these statements, I can understand why Google’s algorithm gives it a high magnitude score. It’s full of dramatic adjectives like best and love.

Conclusion

The CNBC article reviewing Apple’s new iPhoneXS do seem dramatic and emotional. It am pretty impressed that Google Cloud Natural Language can understand that. In part 2, I will dive deeper into articles that have low and high scores from both sources.