Recently, we wrote about natural language processing (NLP). Now, let’s take that concept further by examining sentiment analysis.
Like NLP, sentiment analysis is a way to apply machine learning to glean insights from data. With sentiment analysis, the aim is to understand the feel and the tone in a given segment of text. These sentiment analysis models primarily focus on polarity, meaning how positive, negative, or neutral the text reads. More advanced models can go beyond that, analyzing the emotions involved — happy, sad, angry — and even intentions (i.e. willing to purchase a product vs. not interested at all).
Why Is Sentiment Analysis Useful?
On the surface, sentiment analysis may not seem groundbreaking, but there’s a tangible benefit for businesses that utilize this technology.
IBM’s Watson Blog estimates that “80% of the world’s data is unstructured,” meaning this data is floating around with no form and no organization until something can grab it, filter it, and interpret it.
Sentiment analysis, then, can provide structure and meaning, providing a business more useful data to make decisions.
How Sentiment Analysis Works
A common application of sentiment analysis occurs on social media, where products can access social media platforms via API and analyze unstructured data — mentions, replies, hashtags, and more — involving a given brand, then use sentiment analysis to determine if these conversations are positive, negative, or neutral.
Going a step further, these tools can also run comparisons via the same methods. For example, a recent case study published in the Journal of Physics Conference Series examined nearly 100,000 combined tweets surrounding popular apparel brands Nike (54,788 tweets used) and Adidas (45,062 tweets used).
From there, the research applied sentiment analysis and concluded that the positive polarity of Adidas (27.5%) was higher than that of Nike (24.5%), while Nike’s neutral polarity (63.6%) and negative polarity (11.9%) were both higher than Adidas’ (61.1% and 11.7%, respectively).
This information may lead to Nike investing more in building consumer trust, but it can also inform stakeholders of the current health and perception of each brand.
As sentiment analysis tools continue to develop and integrate with our numerous forms of online communication, so too will their impact on businesses and investors.
Are you using sentiment analysis for your business? Do you need to find engineers with a background in machine learning, artificial intelligence, or data science? Contact us at firstname.lastname@example.org.