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The Good, the Bad, and the Neutral

Making the business case for why Twitter Sentiment Analysis is useful in today's world

Opinion

Photo by Marten Bjork on Unsplash
Photo by Marten Bjork on Unsplash

In just a few weeks, we will be one year removed from the declaration of a global health pandemic by the World Health Organization. It goes without question that we have had to adapt to a new set of circumstances and adopt new practices to ensure our safety.

One of the changes we have had to make is the elimination of large public gatherings. The onset of COVID-19 eliminated in person attendance at concerts, sporting events, and the movies. For companies like Apple and Google, this meant they had to rethink how they would go about hosting an event like WWDC or Google I/O. Typically these events would offer the opportunity for a tech company to debut new products to a select audience and provide information about how their companies are on the leading edge of tech innovation.

However, these tech conferences are not just meant to serve the audiences. A live, in-person audience can provide instant feedback and allow a company to gauge whether or note their products are well-received. After the conclusion of a presentation for a new iPhone, a company like Apple would have demo units available for attendees to examine and test out new features. If this new iPhone was truly revolutionary and something worth talking about, then we might expect members of that select audience to turn to social media to share their thoughts and opinions, which allows the discussion surrounding a product to continue and reach a broader audience. Obviously, with no in-person events the typical transfer of information from event attendee to the larger global audience was broken.

Photo by Teemu Paananen on Unsplash
Photo by Teemu Paananen on Unsplash

Despite the pandemic cutting off the in-person component of this information pipeline, the presence and use of social media has remained constant (if not amplified) throughout 2020. The social media response to a product has therefore become the primary way in which a company can receive feedback under these circumstances. And with social media being the primary feedback system, it becomes all the more valuable to be able to evaluate the sentiment of those social media posts concerning their product.

NLP to the rescue!

Fortunately, the field of natural language processing (NLP) would allow for a tech company to train and build a model that would evaluate the sentiment of tweets. Given that we might expect the debut of a new product to become a trending topic on a platform like Twitter, it would be in a company’s best interests to analyze the sentiment as quickly as possible. Per market research from Twitter, 3 in 4 users said they turned to other Twitter users to learn more about a topic. Twitter also found from this research that 9 in 10 users "changed their opinion about a purchase because advice was given to them by someone they knew on Twitter".

Photo by You X Ventures on Unsplash
Photo by You X Ventures on Unsplash

This means that good tweets can be extremely beneficial to a company while negative tweets can hurt the bottom line. A trained model can run through thousands of tweets quicker than a group of humans can so at a minimum it offers an efficient way to gauge general sentiment on a social media platform. Let us consider what happened to Xbox and Robinhood a few weeks ago to put this in perspective.

Gold makes monsters of men.

-Erin Bowman, Vengeance Road

On January 22, 2021 Xbox decided to raise the price of their Xbox Live Gold subscription from $60 per year to $120 year (in addition to increasing the price for 1 month, 3 month, and 6 month terms). There was swift backlash via Twitter and from gaming media outlets decrying the decision. An article from Techcrunch described it as an "attempt at kicking some bad news under the rug". During the pandemic, video games have certainly served as an escape for many and allowed people another avenue to stay connected to friends and family. When also considering that many individuals (some of them likely to be Xbox gamers) have seen a reduction in pay during the pandemic, many wondered why Microsoft would institute a price hike. After all, why increase the price of the subscription services when they just released two new consoles in November 2020?

It’s safe to say that the court of public opinion did not rule in favor of Microsoft’s decision. In fact, in less than 24 hours, Microsoft announced via Twitter that they "missed the mark" and it would be reversing it’s pricing decision. The significance of this anecdote is twofold:

  • The negative sentiment towards Microsoft’s decision started on Twitter, was subsequently escalated by reputable and prominent gaming media outlets, and finally ended via Twitter with an admission of shortsightedness from Microsoft.
  • The potential gains in revenue from an increase in subscription pricing did not outweigh the potential losses from existing Gold members unsubscribing and the lingering, negative impression of corporate greed.

You know somethin’, Robin. I was just wonderin’, are we good guys or bad guys?

– Little John, Disney’s Robin Hood

Only a few days after we saw Microsoft upset it’s fanbase, the Robinhood app found itself in a similar place. In late January 2020, hedge funds had decided to bet against Gamestop stock, assuming that it’s stock price would plummet given that an ever increasing digital presence in the video game industry was only making it more difficult for Gamestop to survive. However, several people banded together via the subreddit r/WallStreetBets and decided that they would thwart the hedge funds and seek to make their own profit by buying up shares of Gamestop stock. Many of these people turned to apps like Robinhood to purchase their stock. Unfortunately for the Reddit traders, Robinhood made the decision to halt the trading of Gamestop stocks, thereby restricting the potential for individual gain while also preventing the hedge funds from complete financial ruin.

As you might have guessed, the halt to trading was not well received by the general public. Robinhood quickly became a trending topic on Twitter for the wrong reasons and some users chose to head to the Google Play store and leave negative reviews for the app. The app rating plummeted to 1 star. Those reviews were subsequently removed from the Google Play store with Google arguing that they violated the terms of service. Nonetheless, the social media response to Robinhood’s decision has likely left an indelible mark on their reputation. At the time of writing, the SEC is investigating Robinhood’s decision to halt trading, so it seems that the negative sentiment expressed on social media may have been an ominous sign for future repercussions.

So how necessary is an NLP model?

It’s important to note that even with the implementation of an NLP model that could analyze and evaluate sentiment on Twitter, it represents a reactive approach. In this case, the tweets represent the raw data that has to be transformed and fed into the model. The tweets can only come about based on the actions of the company. For the Microsoft and Robinhood examples above, the model would be a tool to help those companies inform their immediate and long-term decision making. It wouldn’t be surprising at all to see a model like this already in use at a tech giant like Microsoft, especially when you consider their decision to reverse the price hike in less than 24 hours. I believe that even though social media sentiment analysis represents a reactive approach, it still can be immensely valuable. Large, for-profit, tech companies care about their branding and always want to improve the bottom line, therefore the implementation of sentiment analysis model is an undertaking worth pursuing.

For one of my projects for my Data Science bootcamp at Flatiron School, I built a Twitter Sentiment Analysis model using tweets from the SXSW festival in 2011. This coincided with the release of the iPad 2 and the announcement of the social media platform Google+. You can find the data for this project here and here is the link to my Github repo. Any and all feedback is welcome!

Thanks for reading! Feel free to connect with me via LinkedIn or Twitter.


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