Not so long ago, computers could only understand commands after compiling them into a machine language. That meant they could not understand the unstructured nature of human speech and writing at all. Helping computers understand what people write or say takes a lot of work. After all, s
In the past, only manual effort could translate human communication into the proper formats and instructions for computers. Today’s natural language processing and text mining platforms can help provide a bridge between the way people communicate and how computers understand things.
Machine understanding of human speech can offer value. As IBM’s article on text mining points out, 80 percent of the world’s data arrives unstructured, like typical human speech and writing. With improved insights from unstructured data, organizations could use these assets for analysis and making decisions. Find out more about natural language processing and text mining platforms to understand their benefits and limitations.
Text mining and NLP relate to each other. Text mining applications use NLP to function. To understand these two terms, consider these brief definitions.
Natural Language Processing
Natural language processing, or NLP, refers to software that understands language almost the same way that people do. According to IBM, this discipline relies on AI, machine learning, and computational linguistics to help machines parse the meaning of unstructured text and speech.
Most people encounter NLP regularly these days. For instance, autocomplete functions, voice assistants, and search engines all rely on NLP.
Text mining uses NLP, and other techniques to glean information from unstructured data and transform it into a format more conventional software or people can use. For clarity, in the case of text mining, text can refer to both spoken and written words.
For instance, companies might deploy text mining to parse feedback from social networks or customer service calls and feed it into another system for analysis and reporting. As another example, text mining could summarize unstructured information for reports that human analysts can use.
Who Benefits From Text Data Mining?
Almost every organization can benefit from text mining. As a simple example, a customer service department might scan social posts or phone calls for feedback. In turn, the department could analyze trends and use this information to make improvements that keep customers happier.
According to Expert.ai, an artificial intelligence solutions provider, some industries that already rely on text mining applications include search engines and social sites, law enforcement, and digital advertising. Within all kinds of organizations, risk managers, fraud and spam prevention teams, and customer care often use text mining tools.
How Do Text Mining Platforms Work?
As with other tools, text mining systems can vary considerably. Sometimes, users have to upload documents or spreadsheets with the data to process. However, more advanced tools should offer an API to integrate with common input sources, like phone records, email, and social networks. Sometimes, users can point their text-mining tool at the data source.
Similarly, mining platforms may offer a bridge to inject the finished product into another system. For example, some tools can send data, tags, and categories for insertion into a records management system or an analytics app.
Features of Text Mining Products
Some typical features of text data mining platforms include:
- Summarizing: The software can scan long documents and produce short summaries. Many businesses use this feature to create archives or to maintain information management systems.
- Tagging and categorizing: These systems can help with data management by determining relevance and adding tags and categories to stored files. This feature helps make searching for specific data sources more efficient.
- Analyzing sentiment: Some of these platforms can determine the sentiment expressed and know if it’s negative or positive. As an example, businesses can use this feature to ensure they respond rapidly to various types of feedback they receive.
- Determining intent: Organizations can serve customers and work more efficiently by using tools to assess intent automatically. For example, the tool might automatically route a company message to sales, customer service, or human resources, depending upon the text.
- Structuring data: The applications might retrieve specific fields from documents and produce a structured file or human-readable report. This feature will make integrating unstructured data into other applications simple.
- Boolean queries: Some tools may let users use logic operators to refine searches. Using keywords with such operators as “AND,” “NOT,” and “OR” may yield better results.
Pros and Cons of Text Mining Platforms
The International Journal of Engineering Research and Technology published a good overview of the advantages and challenges of using today’s text mining platforms.
Significant Benefits of Text Data Mining
To illustrate the benefits, they pointed out that recent increases in the amount of available data make these tools valuable. Over the past several years, the internet, new sensors, and the IoT have generated ever-increasing amounts of data. Handling this information causes challenges. For instance:
- The amount of data makes manual processes too slow and inefficient.
- Even the most talented analysts struggle to make sense of such large datasets without the right tools.
The paper also pointed out that the value of big data does not rest on its size. Organizations may find this data valuable because they can glean insights from the analysis. At the same time, the quantity makes analysis and even deciding which information offers value a challenge. Businesses can use various features of text mining tools to help overcome this obstacle.
Potential Drawbacks of Text Data Mining
Even though text mining can offer good value, almost anybody can guess some of the most obvious drawbacks. People joke about funny and inappropriate phrases that autocomplete and autocorrect functions sometimes provide. Developers try to emulate human understanding as well as possible, but few people would ever mistake them for a fluent human.
Some of the most advanced tools perform very well. Most of these tools use machine learning, so they need training. They learn as they process documents, so they get better at their jobs with experience, much like people. Also, they may require some human intervention during the training phase. Thus, good platforms should provide directions to help people understand how to guide their innovative tools through this process.
On a large scale, automation can do more than people. When it comes to supplying the best answer for each individual block of text, automation still falls short. As with all software tools, some function much better than others. Thus, people still have to understand the capabilities of the tools they use and set their expectations accordingly.
Top NLP and Text Mining Tools to Consider
Consider these well-known text mining tools.