- Text mining involves converting unstructured textual data into a structured format to uncover meaningful patterns and insights.
- Text data exists in various formats within databases, including structured, unstructured, and semi-structured data, with approximately 80% of global data existing in unstructured formats.
- Leveraging text mining tools and natural language processing techniques enables organisations to transform unstructured documents into structured data, facilitating analysis and enhancing decision-making processes.
Text mining involves transforming unstructured textual data into a structured format to reveal valuable patterns and insights. It enables the examination of large volumes of text to detect important concepts, trends, and underlying connections. By harnessing analytical techniques and natural language processing capabilities, text mining enables businesses to extract valuable insights, driving enhanced decision-making and improved operational efficiency.
What is text mining?
Text mining, also referred to as text data mining, entails the conversion of unstructured textual data into a structured format to uncover meaningful patterns and novel insights. It facilitates the analysis of extensive collections of textual materials to identify significant concepts, trends, and latent relationships.
Through the application of sophisticated analytical techniques such as Naïve Bayes, Support Vector Machines (SVM), and other deep learning algorithms, organisations can delve into their unstructured data to unearth concealed associations.
Text data exists in various formats within databases, categorised as follows:
Structured data: This data adheres to a standardised tabular format with numerous rows and columns, simplifying storage and processing for analysis and machine learning algorithms. It typically comprises inputs like names, addresses, and phone numbers.
Unstructured data: This data lacks a predetermined format and includes textual content sourced from platforms such as social media or product reviews, along with rich media formats like video and audio files.
Semi-structured data: Exhibiting a blend of structured and unstructured characteristics, this data possesses some organisation but lacks the structure required by a relational database. Examples include XML, JSON, and HTML files.
Given that approximately 80% of the world’s data exists in unstructured formats, text mining holds significant value for organisations. Leveraging text mining tools and natural language processing (NLP) techniques, such as information extraction, enables the transformation of unstructured documents into a structured format, facilitating analysis and the generation of actionable insights. Consequently, this enhances organisational decision-making, leading to improved business outcomes.
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Text mining techniques
The text mining process encompasses several activities aimed at extracting information from unstructured text data. Text preprocessing, the initial step in this process, involves cleaning and formatting text data for analysis. It encompasses techniques such as language identification, tokenisation, part-of-speech tagging, chunking, and syntax parsing to prepare data for analysis.
Once text preprocessing is complete, various text mining algorithms can be applied to derive insights from the data. Common text mining techniques include:
Information retrieval (IR): IR systems retrieve relevant information or documents based on predefined queries or phrases. This involves sub-tasks such as tokenisation, which breaks text into sentences and words (tokens), and stemming, which extracts the root word form to enhance information retrieval efficiency.
Natural language processing (NLP): NLP enables computers to understand human language in both written and verbal forms. It involves tasks like summarisation to condense text into concise summaries, part-of-speech tagging to assign grammatical tags to tokens, text categorisation for classifying documents based on topics, and sentiment analysis to detect emotions in text.
Information extraction (IE): IE identifies and extracts relevant data from various documents, focusing on structured information. Sub-tasks include feature selection and extraction to enhance the accuracy of predictive models, as well as named-entity recognition to identify and categorise specific entities such as names and locations.
Data mining: Data mining involves identifying patterns and extracting insights from large datasets, including both structured and unstructured data. While text mining falls under the umbrella of data mining, it specifically focuses on structuring unstructured textual data to generate novel insights.
Also read: AI platform Writer launches feature for text generation from images
Text mining applications
Customer service: Companies employ diverse methods to gather customer feedback, ranging from chatbots and customer surveys to NPS (net-promoter scores), online reviews, support tickets, and social media profiles. Integrated with text analytics tools, these feedback mechanisms enable businesses to swiftly address customer concerns and enhance satisfaction levels. Text mining, coupled with sentiment analysis, aids in prioritising critical customer pain points, empowering companies to respond promptly to urgent issues in real-time.
Risk management: In risk management, text mining offers valuable insights into industry trends and financial markets. By monitoring shifts in sentiment and extracting data from analyst reports and whitepapers, organisations, especially banking institutions, gain confidence in assessing business investments across diverse sectors. The application of text analytics for risk mitigation is evident in the strategies adopted by entities like CIBC and EquBot.
Maintenance: Text mining provides comprehensive insights into the operation and functionality of products and machinery. Over time, it automates decision-making processes by identifying patterns associated with issues and recommending preventive and reactive maintenance procedures. Maintenance professionals leverage text analytics to swiftly diagnose the root causes of challenges and failures, streamlining maintenance operations.
Healthcare: Text mining techniques play a crucial role in biomedical research, particularly in information clustering. Manual examination of medical literature is both time-consuming and expensive. Text mining offers an automated approach to extracting valuable insights from vast volumes of medical research, aiding researchers in identifying relevant information efficiently.
Spam filtering: Spam emails often serve as gateways for cyber-attacks, posing security risks to computer systems. Text mining serves as an effective tool for filtering and blocking spam emails, enhancing user experience and minimising the threat of malware infections.
