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Introduction to text mining
Text mining also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning.
Text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. The term is roughly synonymous with text mining.
The term is also sometimes used in a narrower sense to refer to applications that focus on the statistical or predictive aspects of text analytics, or that analyze text to improve search engine effectiveness. Text mining is the process of extracting meaningful patterns from large amounts of unstructured text data. It is a relatively new field that combines elements of computer science, artificial intelligence, and statistics.
Text mining applications
The goal of text mining is to turn unstructured text data into structured data that can be analyzed to reveal valuable insights, such as customer sentiment or market trends.
Text mining can be used to analyze a variety of text-based data, including social media posts, customer reviews, survey responses, and more.
There are many different text mining applications, each with its own set of benefits and drawbacks. Here are four of the most popular text mining applications:
- Social Media Text Mining
Social media text mining can be used to analyze social media posts to understand customer sentiment, identify market trends, and more. - Customer Review Text Mining
Analyze customer reviews to understand customer sentiment, identify product issues, and more. - Survey Response Text Mining
- Email Text Mining
Benefits:
• Can be used to understand customer sentiment
• Can be used to identify product issues
• Can be used to monitor competitor activity
Drawbacks:
• Requires a large amount of data
• Can be time-consuming
Text mining software
Text mining software typically uses natural language processing (NLP) algorithms to process text data. NLP is a field of Artificial Intelligence that deals with the interaction between computers and human (natural) languages.
There are a number of different text mining software packages available. Some packages are free, while others are commercial products.
The most popular text mining software packages are:
- Apache OpenNLP2.
- Stanford CoreNLP3.
- NLTK4.
- TextBlob5.
- spaCy
Each text mining software package has its own strengths and weaknesses. It is important to choose a package that is well suited for the task at hand.
Text mining can be used for a variety of tasks, such as sentiment analysis, topic modelling, and text classification.
Sentiment analysis is the task of determining the sentiment of a text document. This can be useful for a variety of applications, such as customer service and marketing.
Topic modelling is the task of identifying the topics present in a text document. This can be useful for tasks such as document summarization and classification.
Text classification is the task of assigning a label to a text document. This can be used for tasks such as spam detection and topic classification.
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