Text Mining

Text Mining | Informatics Specialization Track

Text Mining

Textual Data Mining Specialization Track

A specialization track focusing on the analysis of text-based data to discover information, patterns, trends, and knowledge from various textual sources.

What is Text Mining?

Text Mining is a field of Informatics that studies how meaningful information can be extracted from textual data. Text data may come from articles, social media, documents, news, user reviews, comments, emails, reports, and other digital sources.

Through this specialization track, students will learn how text is processed, analyzed, grouped, classified, and used to support data-driven decision making. The approaches may involve statistics, machine learning, natural language processing, and information retrieval techniques.

Main Areas of Study

Text Analysis

Understanding the structure and meaning of textual data to identify patterns, trends, and important information.

Natural Language Processing

Learning techniques that enable computers to process and understand human language.

Document Classification

Grouping text into specific categories, such as news topics, types of opinions, or document types.

Information Retrieval

Developing systems that retrieve documents or text-based information in a relevant and efficient way.

Examples of Applications

The Text Mining specialization track has many real-world applications, especially in systems that use large volumes of textual data.

  • Sentiment analysis from customer reviews or social media comments.
  • Fake news or misinformation detection.
  • Automatic document classification based on topics.
  • Text-based information search systems.
  • Chatbots or automatic question-answering systems.
  • Automatic document summarization.
  • Public opinion analysis on products, services, policies, or social issues.
  • Spam detection in emails, comments, or digital messages.

Required Track Courses

In the Text Mining specialization track, students take six required track courses as part of their field-specific study.

No. Course Short Description
1 Web Textual Data Processing Studies techniques for collecting, cleaning, and processing text data from web-based sources.
2 NoSQL – Graph Database Introduces non-relational data management and the representation of data relationships using graph databases.
3 Text Mining Discusses methods for extracting information, patterns, and knowledge from collections of textual data.
4 Textual Information Retrieval Studies concepts and techniques for searching documents or information based on text.
5 Machine Learning for Textual Data Explores the application of machine learning for classification, prediction, and analysis of textual data.
6 Natural Language Processing Studies techniques for processing human language using computers, such as tokenization, feature extraction, and text meaning analysis.

Competencies Developed

Ability to perform text data preprocessing.
Ability to build text classification and clustering models.
Ability to apply natural language processing techniques.
Ability to develop textual information retrieval systems.
Ability to analyze opinions, sentiments, and information patterns from text.
Ability to apply machine learning to textual data.

Career Opportunities and Research Topics

Career Opportunities

  • Data Analyst
  • Text Mining Specialist
  • Natural Language Processing Engineer
  • Machine Learning Engineer
  • Information Retrieval Developer
  • Business Intelligence Analyst

Example Research Topics

  • Sentiment analysis of application user reviews.
  • Fake news detection in Indonesian-language news.
  • Classification of public comments on social media.
  • Academic document search system.
  • Automatic summarization of news articles.
  • Natural language processing-based chatbot.

Who is Suitable for This Track?

The Text Mining specialization track is suitable for students who are interested in data analysis, artificial intelligence, machine learning, natural language processing, and the development of systems that can understand and utilize information from text.

Specialization Track: Text Mining


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