Why is the Popularity of Text Mining as an Analytics Tool Increasing?
The ability to amass a wealth of data means little if you cannot use it to your advantage. Today’s age has been proliferated with content of all sorts, yet the ability to gain valuable insights from this data has become paramount. For this reason, text mining or text analytics has become critical to any business’s success. Text mining helps businesses find the perfect balance to hone in on their strengths and remedy any weaknesses presented in the extracted reports from the data.
In today’s world connectivity, people are fully aware of the power they have over businesses and brands. Each online review is given a weightage because prospective users will look towards these reviews to decide about buying a specific product. The question ‘why is the popularity of text mining as an analytics tool increasing’ is the result of the importance given by companies to online reviews towards their businesses.
What is Text Mining?
Text or data in today’s data-centric world is never in the form we want or can use. This unstructured data is a hassle rather than an asset, as it has to be organized into a machine-readable format to make any sense. For this purpose, programming processes such as text mining are employed to help make sense of the available data in hand.
Text mining is the science of converting the unstructured data into a more organized manner so that the program can perform different types of analysis. Different businesses can use this data for various analyses including sentiment analysis and even fraud detection. Whatever the purpose, text mining is a scalable and quick process that can help enterprises to improve themselves over time and even manage their online presence in real-time.
How Text Mining Works?
Text mining is extracting text from provided datasets by integration of advanced tools such as deep learning and neural networks. These datasets can represent any data from the personal credentials of users of a telecom company or travelers commuting a particular stretch of motorway. Whatever the data, text mining using advanced technologies such as neural networks and natural language processing to make the text compatible with different algorithms. The foremost step in performing text mining is to gather data from various available sources online.
As explained above, this dataset could represent any set of information that we want. Whatever the information, we have to make this data into machine-readable form. For example, letters in a business organization need to be interpreted and organized into different subject themes. These letters, which may be in physical form, must be scanned, and then OCR or Optical Character Recognition is performed, so the printed letters are now readable by computer systems.
Now that a computer can read the data, it is parsed via natural language processing. It is here that a program converts the text into code that is understandable by a machine. The coding breaks the reader into linguistic components, and the program user provides syntactic and lexical training. Finally, the data is ready to be put through the different algorithms for analysis, whether qualitative or quantitative. Reports are generated for a business’s specific needs based on the inputs.
What is a topic model?
An essential aspect of the natural language processing tool is topic modeling. Topic modeling is used to find the theme of a given text. Topic modeling extracts the valuable attributes from a group of words (known as corpus) and connects similarly phrased words to find the theme of a sentence/phrase. The tool learns to characterize how certain words occur concurrently in a sentence. The tool can thus find co-existing clusters in new data, thereby finding the inherent theme of the text.
Difference Between Text Mining and Text Analytics
Text mining and text analytics are similar concepts and are often used interchangeably; however, they have a fundamental difference in their process. Text mining is a process to identify valuable information from within a text. The nature of the information provided by text mining is qualitative, while text analytics can deliver both qualitative and quantitative information about a dataset.
Moreover, text analytics can find patterns, visualizations, and quantitative properties of a given dataset. Meanwhile, text mining’s primary focus is to analyze the given text. Text mining also employs modern tools such as NLP, statistical models, linguistics, machine learning, and deep learning models to interpret the data according to a user’s needs.
Text Mining Challenges and Issues
However intelligent or advanced, machines and computers cannot replace human intelligence. Human language and its nuances remain difficult for computers to process. Despite advancements in artificial intelligence and deep learning methods, a hundred percent text mining accuracy is still impossible.
Human language is full of features that even humans sometimes fail to understand. Some people, for example, fail to grasp sarcasm. Likewise, systems and computers cannot be expected to learn such complex human sentiments quickly. Training is required before models can interpret text the way we do.
How is Text Analysis Accuracy Measured?
Precision and Recall are the two parameters measuring the accuracy of text mining results. Precision measures a model’s correctness each time it predicts something. Meanwhile, Recall tells us how many times the model was able to detect the topic from a given data. Hence higher the Recall and precision, the higher the accuracy, as the model can correctly extract all the instances of a given feature in a text.
Applications of text mining
The importance of text mining as an analytics tool is indeed increasing with each passing day. Computer models are getting closer to mimicking human intelligence despite the challenges of interpreting human language and sentiments. It is true of text mining and its applications in sentiment analysis. The ability to derive sentiments and human emotions from raw data and text is exceptional.
We have tried to answer the question, ‘why is the popularity of text mining as an analytics tool increasing?’ by highlighting the benefits and procedures of text mining. Programming applications and Artificial intelligence tools have made raw data beneficial for businesses and brands.
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