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 data sources and extracting relevant data and insights. With innovations in AI, text analytics models can now be built on a no-code NLP platform.
In this blog, we’ll review how users can leverage a no-code NLP platform to build text analytics models that make unstructed text mining more efficient.
So Much Text Data, So Many Tools
With unstructured data making up nearly 80% of global data, enterprises and, in particular, financial services companies are giving significant attention to text mining. However, with so many different tools available, selecting the right method of text analytics that generates the most ROI for an organization can be a challenge.
Challenges of Traditional Text Mining
Traditional text mining requires a combination of data retrieval and mining, machine learning, statistics and computational linguistics.
Coding tools like Python are used to write programs to analyze text from unstructured data. Implementing traditional text mining methods is challenging as business leaders must find the right text mining tool, hire subject matter experts and understand the technical aspects of it.
The first step in successfully executing a text mining approach is to ensure clean data is collected. Without reliable and high-quality data sources, financial teams will have unreliable analyses and inaccurate investment signals. Once financial teams are confident in the quality of their data sources, they can then use AI to automate the process of researching and extracting insights from various sources.
Advances in No-Code NLP make developing Models for Text Mining More Accessible
Given the structural challenges and lack of subject matter expertise in AI, financial leaders may be hesitant to invest their company’s resources in these new technologies. It can be extremely costly and time-consuming to restructure an organization so that AI can be implemented. However, with recent advances, a number of companies have emerged to bring no-code AI to technical and non-technical users.
These no-code AI tools now make it possible for Citizen Data Scientists, such as Research Analysts and Business Analysts to quickly and accurately mine text and perform sentiment analysis.
Text Mining & Natural Language Processing
Text mining uses Artificial Intelligence (AI) and Machine Learning (ML) to extract and analyze information from a variety of documents – social media, internal and external documents, emails, instant messages, articles and more. This process has gained popularity with advances in Natural Language Processing (NLP) technologies, which enable a quicker, more accurate and efficient way to research and analyze unstructured data with text analytics models.
NLP is a subset of AI that includes the automated process of classifying documents and extracting data within large sets of structured and unstructured text.
NLP can be used to structure unstructured text and to extract the sentiment, topic, characters, relevance and intent from multiple documents. Combined with data visualization tools, text analytics and NLP can enable companies to understand the story behind their data and make better decisions.
For example, let’s say you need to examine hundreds of Glassdoor reviews to understand employee sentiment around a company. With AI/ML/NLP, text-mining algorithms can extract the most popular topics from the customer comments and analyze topics based on the sentiment — whether the comments are positive, negative or neutral.
Additionally, you can identify keywords regarding a given topic for insights into the company and its products and services. In a nutshell, text mining allows teams to analyze raw textual data on a large scale and extract actionable insights.
No-Code NLP for Text Mining
New technologies like the Accern NoCodeNLP Platform provide out-of-the-box pre-trained NLP models to yield insights that are specific to finance services – for example, asset management firms and banks can use Accern for credit risk assessments and ESG investing, while insurance companies can use Accern for enhancing underwriting and detecting fraud.
Users are often misled by the idea of pre-trained models and ready-made workflows, as they often think that customization is limited. However, that’s not always the case. For example, while the Accern pre-built workflows act as a quick start, every part of the workflow can be modified. Even NLP models can be retrained through an intuitive, no-code NLP model trainer to generate custom insights quickly and efficiently.
Users can either import their datasets from emails, company documents, and CRM systems into the platform, or access integrated datasets from external providers and the platform provides the clean data required for a successful text mining approach.
Understanding if No-Code NLP Right for Your Business
When considering a no-code NLP platform, consider the following five questions:
- What business process can be enhanced with automation?
- What type or types of data do you need to process?
- Is the data located in a place where an AI process can access it – for example, in a data warehouse, CRM or CMS systems?
- What type of insights are you looking to draw from the data?
- How will the insights be consumed – for example, through an API or a dashboard?
These questions can help clarify whether a no-code NLP platform can be used to automate manual processes or improve decision making.
A successful NLP implementation strategy includes:
- Identifying the structural components of bringing NLP into an organization
- Setting clear business and technical objectives
- Ensuring a process for continuous improvement
Schedule a demo to learn more about the Accern NoCodeNLP Platform and how it can drive unstructured data insights for business ROI.
- AI / ML / NLP Data Science Acronyms
- Structured vs Unstructured Data vs Semi-Structured Data (Differences)
- What is NLP Text Summarization: Benefits & Use Cases
- Extracting Information from Unstructured Text with NLP – (6 Ways)