The quantity of digital text information has increased exponentially in the past several years and will continue to grow. From social networking posts to client transactions, surveys, reviews, chats, emails, and much more, companies face the challenge of monitoring various resources and extracting relevant data.
The rise of unstructured data on the internet is a chance for both small and massive enterprises. Along with information from new resources, businesses have found ways to create new insights from unstructured data, leading to new technologies and opportunities for research. With the rapid evolution of large data analytics and with unstructured content making an estimated 80 percent of associations’ data, financial enterprises have given significant attention to text mining.
Text Mining And Natural Language Processing (NLP)
Social networking, external and internal files, emails, instant messages, and posts are a number of the data sources utilized in text analytics. The procedure has gained popularity as NLP enables a quicker, more accurate way to research and analyze unstructured data.
NLP is a subset of AI that contains 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 programs, text analytics and NLP allow businesses to comprehend the story behind their data and make better choices.
For example, let’s say you want to examine hundreds of Yelp reviews to understand customer sentiment around a business. With ML, a text-mining algorithm could extract the most popular topics from the customers’ comments and examine topics based on sentiment — if the comments are positive, negative, or neutral. Additionally, you can identify keywords regarding a given topic for insights into the company and its services and products. In brief, text mining allows teams to examine raw data on a big scale.
Financial enterprises recognize the productivity gain and earnings benefits of executing AI into their teams’ workflows. The worldwide AI marketplace in fintech is expected to reach $22.6 billion in 2025.
Challenges Of Traditional Text Mining Approaches
Coding tools such as Python are used to program machines to analyze text from unstructured data. Finding the ideal text mining tool, hiring subject matter experts, and having leaders with restricted knowledge are a couple of challenges fiscal enterprises face when taking this approach.
The first step in successfully implementing a text mining approach is to ensure clean data is accumulated. With no reliable, high-quality data sources, monetary teams will have unreliable investigations and inaccurate investment signals.
Given the structural difficulties and lack of subject matter experience on AI, some financial leaders are hesitant to invest the company’s resources in AI. Thankfully, with technological advancements and innovation, no-code AI tools now bring nontechnical users text mining capacities.
Programs Of No-Code NLP
Several vendors provide no-code tools that provide ready-made and powerful use cases. Researchers and analysts may choose from pre-trained NLP models to yield particular finance alternatives. Users tend to be misled by the idea of a”ready-made” use case because they often believe that customization is limited. But, that’s not always the case.
SaaS platforms offer you the clean data required for a successful text mining strategy. Users can either import their datasets from emails, company documents, and CRM systems into the platform or get integrated datasets from external suppliers.
But, not all companies are fit to execute AI and ML. By way of example, smaller companies that do not store large quantities of information or need information processing from outside programs will most likely not find a significant advantage from AI.
Developing a Strategy
Before implementing any no-code solutions for text mining, then think about these five concerns:
1. Is there a business process that can be improved with automation?
2. Which kind of data is joined to the use case?
3. Is the information found in a location where an AI procedure can see it? For accurate forecasts, businesses must make sure that new data is generated and fed into the AI system to be processed. AI can find information from CRM programs, Google Analytics, content management systems, imported files, and news websites, among others. Reusing information or not having an extra amount of it won’t provide the same outcomes.
4. What kind of existing and new insights are you wishing to draw from the information?
These questions can help your company understand and create a strategy around how AI and ML are going to be employed to maximize work efficiency and development. An effective strategy includes identifying areas of improvement, setting clear objectives, and ensuring a continuous process for advancement to comprehend in which AI can fit into the corporate DNA.
Remember: Clean data is an integral element of a successful AI solution. The more info AI is given, the better the answer becomes. So organizations that have more data can understand their customers better. On the other hand, with no tools to examine huge data, it is simply futile.