Although Machine Learning algorithms have been around since mid-20th century, this technology along with Deep Learning is the newest popular boy in town, with good reason. Due to recent advances in computing power and data availability, they're being more and more used to perform astonishing tasks.
Your data holds secrets. Uncovering them is absolutely essential to business success. But mining volumes of text-based data for insights poses a big resource challenge for companies, which is why text analytics tools have become so business critical. There are two different approaches in the market machine learning and deep linguistic analysis, as we mention in different articles. In this post, we will dig more in depth in both of them.
In previous posts, we have outlined the crucial role of Machine Learning for Analytics (in Machine Learning & Deep Linguistic Analysis in Text Analytics), and the implications of using Machine Learning for analyzing and structuring text (in What are the limitations of Machine Learning for Text Analysis?).
This post dives into one of the topics of a previous post "Machine Learning & Deep Linguistic Analysis in Text Analytics". We referred to the strong points of Machine Learning technology for insight extraction. We also stated that using variations of classical "bag of words" models limits the ability of Machine Learning to extract insights. Here we go into some detail on this last statement.
Text analysis is becoming a pervasive task in many business areas. Machine Learning is the most common approach used in text analysis, and is based on statistical and mathematical models. Linguistic approaches, which are based on knowledge of language and its structure, are far less frequently used. These two approaches are often seen as alternative or competing approaches.