Text analysis is a new way of approaching machine learning and statistical analytical techniques. As its name suggests, text analysis focuses on text-based data points and offers a brand new approach to market research and other machine learning processes that brands have incorporated as an integral component of their ongoing business intelligence systems.
Many brand managers who are new to this process ask. “What is text analysis?” Indeed, many who are first approaching this analytical field make the mistake of thinking that text mining and text analytics are the same thing. However, text analytics and the analysis products that are derived from them are unique in that brands are able to make sense of both the aggregate data and the context of what users are saying rather than just taking away surface-level insights. Text analysis processes lean heavily on machine learning in order to make sense of large datasets of text-based information. By engaging with these datasets, companies are able to make smarter decisions about product rollouts, marketing campaigns, and many other elements in their sector.
Continue reading to learn more about how text analytics can benefit your machine learning processes and transform your business intelligence suite for the better.
Text analytics rely on connectivity and big datasets.
Text analytics is used in tandem with big data and machine learning processes. This is because learning to make sense of unstructured text data requires large volumes of information and ongoing processes to incorporate lessons learned and increasing levels of context.
One thing that sets text analysis processes apart from other machine learning capacities is the prevalence of open-source data that is taken directly from the internet. Today, more than 4 billion people enjoy stable and routine internet access. And the vast majority of these internet users engage with social media platforms regularly. Social media users contribute huge amounts of data to the field of digital connectivity on a daily basis. Tweets, blog posts, YouTube content, and more populate the digital realm, and their volume balloons with each passing hour.
Content offers great context to brands that can leverage analytical processes that make sense of text-based posts. Through the use of text analysis, brands can leverage their understanding of the marketplace and consumer sentiment for greater mobility and agility in the marketplace at all terms.
Machine learning is a crucial component of this process.
Machine learning is a process within the realm of artificial intelligence (AI), and it focuses on leveraging data and algorithms to mimic the way the human mind learns and understands the world around it. Humans engage with their surroundings through various forms of trial and error. As we work to make greater sense of our environments, we succeed and fail in various ways, and these outcomes of routine tasks shape our understanding of the world and the things in it.
In the branch of computer science that deals with machine learning and artificial intelligence, data scientists have developed algorithmic processes that mimic this learning habit and seek to speed up the process of extracting insights and critical information from large datasets. Text analysis is a textbook example of these processes. By utilizing machine learning frameworks, text analysis software offers users the ability to continuously take in data and grow in their understanding of the market in which they compete.
The truth is that text analysis and the insights that are derived from it offer crucial points of mobility for businesses of all varieties and across sectors. Consider implementing these frameworks in your own analytical processes for greater command of the market as a whole. Machine learning and text analytics are essential for business success. Use them in your own office today.
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