How No-Code Solutions Aid Text Mining In Big Data Analytics Posted on : Nov 17 - 2020

The quantity of digital text data has grown exponentially in recent years and will continue to grow. From social media posts to customer transactions, surveys, reviews, chats, emails and more, businesses face the challenge of monitoring various sources and extracting relevant data.

The rise of unstructured data on the internet is an opportunity for both small and large enterprises. Along with data from new sources, businesses have found ways to generate new insights from unstructured data, leading to new technologies and opportunities for research. With the rapid evolution of big data analytics and with unstructured content making up an estimated 80% of organizations' data, financial enterprises have given significant attention to text mining.

Text Mining And Natural Language Processing (NLP)

Text mining, or text analytics, extracts and analyzes information from a vast array of documents by using artificial intelligence (AI) and machine learning (ML). Social media, internal and external documents, emails, instant messages and articles are some of the data sources used in text analytics. The process has gained popularity as NLP enables a quicker, more accurate way to research and analyze unstructured data.

NLP is a subset of AI that includes the automated process of classifying and extracting text within large sets of unstructured text. Data can be extracted by sentiment, topic, characters, relevance and intent. Combined with data visualization tools, text analytics and NLP enable companies to understand the story behind their data and make better decisions.

For example, let's say you need to examine hundreds of Yelp reviews to understand customer sentiment around a company. With ML, a text-mining algorithm can extract the most popular topics from the customers' comments and analyze topics based on sentiment — whether the comments are positive, negative or neutral. Additionally, you can identify keywords regarding a given topic for insights around the company and its products and services. In a nutshell, text mining allows teams to analyze raw data on a large scale.

Financial enterprises recognize the productivity gain and revenue benefits of implementing AI into their teams' workflows. The global AI market in fintech is expected to hit $22.6 billion in 2025. With technological advances, text mining capabilities are evolving to become more accessible and easily deployable. View More