Kyle M Haberman

Major: BBA MIS
Graduation: May 2019


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Leveraging Sentiment Analysis

As the importance of data analytics has become apparent, companies have invested in new tools and processes to leverage data to make better decisions. One of these powerful developments is sentiment analysis. Sentiment analysis is the process of analyzing text and assigning an emotion to such text to understand how the author feels about a subject (“Sentiment Analysis,” 2017). This process is commonly used to examine social media posts to observe the reactions that people have towards a brand, product, or service. This ability to obtain a large amount of feedback can be highly beneficial for a company as they can use such feedback to understand what they are doing well, what they can improve upon, and how they compare to their competition. In turn, businesses can act on the conclusions they find to gain an advantage and make a higher profit.

Overall, the goal of MIS2502 is to teach students how to manipulate data to solve business problems. Sentiment analysis builds on this idea of turning data into valuable information, which was specifically covered near the beginning of the semester. Basically, sentiment analysis software quantifies and classifies raw data that is in the form of text, which is used to develop information − such as the identification of the most well-received brand in an industry. The application of sentiment analysis can be demonstrated through Taco Bell’s use of the technology. After teaming up with a social media listening firm, Taco Bell used sentiment analysis to identify 3.5 million brand advocates that were not already following them on social media (Hanlon, 2016). Essentially, Taco Bell can use this information to pinpoint the number of potential customers they could reach through a new advertising campaign. Undoubtedly, sentiment analysis can provide significant value to help businesses make predictions and manage their brand.



Hanlon, Patrick. (2016). Emotional Branding Now Unlocked By Big Data. Forbes. Retrieved from

Sentiment Analysis. (2017). Lexalytics. Retrieved from

Professional Achievements

    • Class project Spring 2017

    • Spring 2018 MIS3502 Section 003Spring 2018

    • Fox IT Career FairFall 2016, Fall 2017, Fall 2018

    • IBIT small group meeting with industryFall 2016, Fall 2016, Fall 2017

    • IBIT/CDI Challenge or ConferenceFall 2016, Fall 2018

    • Active AIS memberFall 2015, Spring 2016, Fall 2016

    • Fox IT AwardsSpring 2016, Spring 2017

    • Active TDAP memberFall 2015, Spring 2016, Fall 2016, Spring 2017

    • AIS professional development workshopFall 2015, Spring 2016, Fall 2016, Spring 2017, Fall 2017

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