research topic text mining

Text mining, also known as text data mining, is the process of transforming unstructured text into a structured format to identify meaningful patterns and new insights. You can use text mining to analyze vast collections of textual materials to capture key concepts, trends and hidden relationships.

By applying advanced analytical techniques, such as Naïve Bayes, Support Vector Machines (SVM), and other deep learning algorithms, companies are able to explore and discover hidden relationships within their unstructured data.

Text is a one of the most common data types within databases. Depending on the database, this data can be organized as:

• Structured data: This data is standardized into a tabular format with numerous rows and columns, making it easier to store and process for analysis and machine learning algorithms. Structured data can include inputs such as names, addresses, and phone numbers.
• Unstructured data:  This data does not have a predefined data format. It can include text from sources, like social media or product reviews, or rich media formats like, video and audio files.
• Semi-structured data: As the name suggests, this data is a blend between structured and unstructured data formats. While it has some organization, it doesn’t have enough structure to meet the requirements of a relational database. Examples of semi-structured data include XML, JSON and HTML files.

Since  roughly 80% of data in the world resides in an unstructured format  (link resides outside ibm.com), text mining is an extremely valuable practice within organizations. Text mining tools and  natural language processing  (NLP) techniques, like  information extraction  (link resides outside ibm.com), allow us to transform unstructured documents into a structured format to enable analysis and the generation of high-quality insights. This, in turn, improves the decision-making of organizations, leading to better business outcomes.

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Read the guide for data leaders

The terms, text mining and text analytics, are largely synonymous in meaning in conversation, but they can have a more nuanced meaning.  Text mining and text analysis identifies textual patterns and trends within unstructured data through the use of machine learning, statistics, and linguistics. By transforming the data into a more structured format through text mining and text analysis, more quantitative insights can be found through text analytics. Data visualization techniques can then be harnessed to communicate findings to wider audiences.

The process of text mining comprises several activities that enable you to deduce information from unstructured text data. Before you can apply different text mining techniques, you must start with text preprocessing, which is the practice of cleaning and transforming text data into a usable format. This practice is a core aspect of natural language processing (NLP) and it usually involves the use of techniques such as language identification, tokenization, part-of-speech tagging, chunking, and syntax parsing to format data appropriately for analysis. When text preprocessing is complete, you can apply text mining algorithms to derive insights from the data. Some of these common text mining techniques include:

Information retrieval

Information retrieval (IR) returns relevant information or documents based on a pre-defined set of queries or phrases. IR systems utilize algorithms to track user behaviors and identify relevant data. Information retrieval is commonly used in library catalogue systems and popular search engines, like Google. Some common IR sub-tasks include:

• Tokenization: This is the process of breaking out long-form text into sentences and words called “tokens”. These are, then, used in the models, like bag-of-words, for text clustering and document matching tasks. 
• Stemming: This refers to the process of separating the prefixes and suffixes from words to derive the root word form and meaning. This technique improves information retrieval by reducing the size of indexing files.

Natural language processing (NLP)

Natural language processing , which evolved from computational linguistics, uses methods from various disciplines, such as computer science, artificial intelligence , linguistics, and data science, to enable computers to understand human language in both written and verbal forms. By analyzing sentence structure and grammar, NLP sub-tasks allow computers to “read”. Common sub-tasks include:

• Summarization: This technique provides a synopsis of long pieces of text to create a concise, coherent summary of a document’s main points.
• Part-of-Speech (PoS) tagging: This technique assigns a tag to every token in a document based on its part of speech—i.e. denoting nouns, verbs, adjectives, etc. This step enables semantic analysis on unstructured text.
• Text categorization : This task, which is also known as text classification, is responsible for analyzing text documents and classifying them based on predefined topics or categories. This sub-task is particularly helpful when categorizing synonyms and abbreviations.
• Sentiment analysis : This task detects positive or negative sentiment from internal or external data sources, allowing you to track changes in customer attitudes over time. It is commonly used to provide information about perceptions of brands, products, and services. These insights can propel businesses to connect with customers and improve processes and user experiences.

Information extraction

Information extraction (IE) surfaces the relevant pieces of data when searching various documents. It also focuses on extracting structured information from free text and storing these entities, attributes, and relationship information in a database. Common information extraction sub-tasks include:

• Feature selection, or attribute selection, is the process of selecting the important features (dimensions) to contribute the most to output of a predictive analytics model.
• Feature extraction is the process of selecting a subset of features to improve the accuracy of a classification task. This is particularly important for dimensionality reduction.
• Named-entity recognition (NER) also known as entity identification or entity extraction, aims to find and categorize specific entities in text, such as names or locations. For example, NER identifies “California” as a location and “Mary” as a woman’s name.

Data mining

Data mining is the process of identifying patterns and extracting useful insights from big data sets. This practice evaluates both structured and unstructured data to identify new information, and it is commonly utilized to analyze consumer behaviors within marketing and sales. Text mining is essentially a sub-field of data mining as it focuses on bringing structure to unstructured data and analyzing it to generate novel insights. The techniques mentioned above are forms of data mining but fall under the scope of textual data analysis.

Text analytics software has impacted the way that many industries work, allowing them to improve product user experiences as well as make faster and better business decisions. Some use cases include:

Customer service: There are various ways in which we solicit customer feedback from our users. When combined with text analytics tools, feedback systems, such as chatbots , customer surveys, NPS (net-promoter scores), online reviews, support tickets, and social media profiles, enable companies to improve their customer experience with speed. Text mining and sentiment analysis can provide a mechanism for companies to prioritize key pain points for their customers, allowing businesses to respond to urgent issues in real-time and increase customer satisfaction. Learn how Verizon is using text analytics in customer service .

Risk management: Text mining also has applications in risk management, where it can provide insights around industry trends and financial markets by monitoring shifts in sentiment and by extracting information from analyst reports and whitepapers. This is particularly valuable to banking institutions as this data provides more confidence when considering business investments across various sectors. Learn how CIBC and EquBot are using text analytics for risk mitigation .

Maintenance: Text mining provides a rich and complete picture of the operation and functionality of products and machinery. Over time, text mining automates decision making by revealing patterns that correlate with problems and preventive and reactive maintenance procedures. Text analytics helps maintenance professionals unearth the root cause of challenges and failures faster.

Healthcare: Text mining techniques have been increasingly valuable to researchers in the biomedical field, particularly for clustering information. Manual investigation of medical research can be costly and time-consuming; text mining provides an automation method for extracting valuable information from medical literature.

Spam filtering: Spam frequently serves as an entry point for hackers to infect computer systems with malware. Text mining can provide a method to filter and exclude these e-mails from inboxes, improving the overall user experience and minimizing the risk of cyber-attacks to end users.

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This paper presents the initial efforts towards the creation of a new corpus on the history domain.

Train and fine-tune an LDA topic model with Python's NLTK and Gensim.

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• Text and Data Mining Guide

Text and Data Mining Guide: Home

What you'll find in this guide.

In this guide you will find the following:

• Getting started with Text Mining Project   - Step-by-step guide on how to get started with your text mining project along with examples of past text mining projects from UW researchers and students.
• Text Mining Tools - Overview of tools for data collection, web scraping, text cleaning and analytics which can be utilized in your text mining project.
• Data Collection Methods - Learn about the various ways to acquire data for your Text and Data Mining (TDM) projects.
• Text Analytics & Visualizations - A beginner's guide to working with text data to perform analysis and generate insightful visualization.

What is Text Mining?

Text Mining, also known as Text Data Mining, is a branch of Artificial Intelligence focused on extracting high-quality information and insights from unstructured textual data . Text Mining is broadly utilized for information retrieval, data mining and knowledge discovery. It uses computational analysis to process large quantities of information. By taking advantage of computers’ ability to find patterns, researchers can identify patterns in texts and data sets . Text mining is often focused on natural language texts, while data mining is focused on large data sets. The data found while text mining can be used to understand the relationships between words and documents.A few common text mining tasks are as follows:

• Text Classification
• Sentiment Analysis
• Topic Modeling
• Document Clustering & Classification
• Entity & Information Extraction
• Text Summarization

How to get started with Text Mining Project?

Before starting a text mining project, it is important to go through the following steps to gauge the feasibility of the project.

• What is your research question you are trying to answer?
• What kind of data is required to address this question?
• Do you need textual data? Where can you find it? A website? PDF? API?
• What is the format of the text data at the identified sources?
• Is the data quality at par for our purpose? If not, how do we improve it or acquire higher quality data?

Sample Text Mining Projects

Which 2020 US Democratic Presidential Nominee said this? Warren? Biden? Sanders?

Using the quotes extracted from debates between candidates we wish to classify which candidate said one of the quotes. Since we have existing labels and want to predict which nominee was recorded saying the new, unseen quote, this is an example of text classification. This project used supervised & semi-supervised learning techniques.

Concepts: Text Classification, Text Preprocessing, Feature Engineering

Natural Language Recipe Parser

To build a database of recipes and its ingredients, we decided to scrape websites to extract recipes using BeautifulSoup, however, almost all food-blogging or recipe curating websites never separate the name of the ingredient from the measurement, quantity and additional description. This natural language recipe ingredient parser can be used by food blogging websites or apps likewise to improve management of opaque ingredients by converting into easy to manipulate & exploit strings stored in tabular format. Thus to extract the ingredients name, quantity and unit we used a custom named entity recognition model which in a given sentence will identify these entities and return them for easy use. 

Concepts: Web Scraping, Entity & Information Extraction, Text Cleaning & Analytics

COVID-19 Twitter Sentiment Analysis

Twitter is a rich mine of opinions and in this project tweets from two states were analyzed - Washington and Florida to understand how people reacted to preventative measures and policies during COVID. This The project hypothesizes that this sentiment will be captured in twitter data and hence can be utilized by the government at all levels to create effective strategies to inform the public better.

Concepts: Sentiment Analysis, Data Extraction using API

Have a Text Mining Question?

The Libraries and eScience Institute offer limited office hour support for text mining. To learn more or schedule a consultation, email [email protected]

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• Last Updated: Mar 15, 2024 12:18 PM
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Text mining & text analysis

• Why use text mining/analysis?
• What can you do with text mining/analysis?
• Text mining/analysis activities or tasks
• Examples of text mining/analysis
• Library databases
• Social media
• Open sources
• Web scraping
• Language Corpora
• Transcription of audio/video data
• Managing large data sets
• Tool directories
• Research methods

Machine learning

Natural language processing, topic modelling, network analysis, visualisations.

• Programming resources
• Considerations - Ethics, Copyright, Licencing, Etiquette
• Further help

Text analysis often relies on machine learning, a branch of computer science that trains computers to recognise patterns. There are two kinds of machine learning used in text analysis: supervised learning, where a human helps to train the pattern-detecting model, and unsupervised learning, where the computer finds patterns in text with little human intervention. An example of supervised learning is Naive Bayes Classification. See Natural Language Processing and Topic Modeling for examples of unsupervised machine learning

• Machine learning - reference entry - Encyclopedia of the Sciences of Learning more... less... UQ sign in required
• Naive Bayes Classification

Natural language processing, a kind of machine learning, is the attempt to use computational methods to extract meaning from free text. Among other things, natural language processing algorithms can derive: names of people and places, dates, sentiment, and parts of speech.

• Natural Language Processing more... less... UQ sign in required
• The Stanford Natural Language Processing Group Natural Language Processing software available to everyone.

Topic modeling, a form of machine learning, is a way of identifying patterns and themes in a body of text.  Topic modeling is done by statistical algorithms, such as Latent Dirichlet Allocation, which groups words into "topics" based on which words frequently co-occur in a text

• Topic Modeling more... less... UQ sign in required
• Using Word Clouds for Topic Modeling Results

Network analysis is a method for finding connections between nodes representing people, concepts, sources, and more. These networks are usually visualised into graphs that show the interconnectedness of the nodes. 

Social network analysis - the process of investigating social structures through the use of networks and graph theory. It characterises networked structures in terms of nodes (individual actors, people, or things within the network) and the edges, or links (relationships or interactions) that connect them. 

Semantic network analysis - a network that represents semantic relations (meanings) between concepts. This is often used as a form of knowledge representation. It is a graph consisting of nodes, which represent concepts, and edges, which represent semantic relations between concepts.

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Text visualisation is a way to "see" your data.  Text mining visualisation can help researchers see relationships between certain concepts.  An example of a visualisation of data can be word clouds, graphs, maps, and other graphics that produce a visual depiction the data.

Various Text Analysis Projects with Visualisations

• With Criminal Intent  - currently unavailable
• The state of our union is... dumber
• Novel Views: Les Miserables
• Tolkien's Books Analysed

Word Frequency Visualisations

• Google n-gram viewer  - word frequencies over time
• Historical culturomics of pronoun frequencies  - pronoun frequencies by gender over time
• The Words They Used  - bubble cloud of words from national convention speeches, with size and color coding
• Ye Shall Know Them By Their Words  - word frequencies by topic for presidential nomination speeches ( additional description )
• Mining Books to Map Emotions  - frequencies of sentiment terms over time
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• UQ Library - Data Visualisation Software
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• Last Updated: May 12, 2024 4:19 PM
• URL: https://guides.library.uq.edu.au/research-techniques/text-mining-analysis

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Text Mining-Based Mental Health Research

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Health informatics is a multidisciplinary field that integrates computer and information science, social and behavioral science, and other fields that deal with information in health and biomedicine, by using patient information to improve healthcare. The burden of depression and other mental health ...

Keywords : Mental health, Text mining, Social media mining, E-mental health, Health informatics, Mental health information, Public mental health

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A survey of the literature: how scholars use text mining in Educational Studies?

• Published: 12 August 2022
• Volume 28 , pages 2071–2090, ( 2023 )

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• Yunjo An 1  

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The massive amount of text related to education provides rich information to support education in many aspects. In the meantime, the vast yet increasing volume of text makes it impossible to analyze manually. Text mining is a powerful tool to automatically analyze large-scaled texts and generate insights from the texts. However, many educational scholars are not fully aware of whether text mining is useful and how to use it in their studies. To address this problem, we reviewed the literature to examine the educational research that used text mining techniques. Specifically, we proposed an educational text mining workflow and focused on identifying the articles’ bibliographic information, research methodologies, and applications in alignment with the workflow. We selected 161 articles published in educational journals from 2015 to 2020. We find that text mining is becoming more popular and essential in educational research. The conclusion is that we can employ three steps (text source selection, text mining techniques application, and educational information discovery) to use text mining in educational studies. We also summarize different options in each step in this paper. Our work should help educational scholars better understand educational text mining and provide support information for future research in text mining for educational contexts.

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Yang, J., Kinshuk & An, Y. A survey of the literature: how scholars use text mining in Educational Studies?. Educ Inf Technol 28 , 2071–2090 (2023). https://doi.org/10.1007/s10639-022-11193-3

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