Updated July 6, 2023
Introduction to Text Data Mining
The multi-disciplinary field where analytics of text is done to derive high-quality information with the help of different patterns and trends through statistical means, be it statistical pattern learning, with different steps such as data collection, natural language processing, information extraction, and data mining so that unstructured data can be transformed into useful information that makes the companies form useful decisions from the facts and relationships derived from the result is called text data mining.
Types of Text Data Mining
Given below are the types of Text Data Mining:
1. Extraction of Data
This is the process where useful information is extracted from unstructured data. Relationships and entities between the relevant data are identified and noted. Also, entities and attributes of the data are noted from the unstructured or semi-structured data. This information is stored in the database. Whenever needed, the patterns of the relationships are checked and used for the businesses or any other needs. The efficiency of the data stored is checked so that the data storage process helps for future needs.
2. Retrieving Data
This is another process where required patterns and sets are extracted from the data based on the user’s needs. For example, the user can search the data with a specific set of words or patterns to easily understand the data. Algorithms are used to identify the user patterns of searching data to provide the relevant information faster. Also, these algorithms help the system identify the user behaviors to be tracked for discovering the same patterns of relevant data. Google search is the best example of retrieving data techniques.
3. Categorizing Data
This process comes under the supervised learning technique in terms of data scientists. This is also called Natural Language Processing. Depending on the data of the database, predefined topics are selected and given to the data. The documents are collected and analyzed to give the right titles to the data and processed for further analysis. This process is easy as if the user knows the relevant topic, they can search with the topic, and hence the analysis of the entire data is not needed. This saves time and the work of the data analysts. Also, it is easy to automate this process in different contexts. In fact, the process is already automated so that spam filtering and categorizing web pages along with other data science techniques can be applied easily to the process of NLP.
4. Clustering
Clustering is one of the famous text mining techniques where data is categorized into small clusters based on the topic or the structures in the text information. The process is hectic as the user does not have to aby prior information of the data; it is difficult to form useful clusters. This is technically a disadvantage of this process. If the clusters are identified, this process acts as a pre-processing technique for other text mining processes as this is a standard method of classifying data. It classifies the data in such a way that all the members in the same group must be more similar to each other than in other groups. This makes the data analysis easy.
5. Text Summarization
As the name suggests, the summary of the relevant texts is formed in this technique. Valuable information is provided to the user by compressing the data automatically. Different text sources are checked so that precise information is formed of the data and the extract is the same as the huge number of original documents. Different techniques are used in the text summarization as different inputs are needed for this process. Neural networks and regression models are the common techniques used in text summarization. The long text is shortened so that a summary is formed of the entire text. This makes the process easy for the users who go through the summaries to retrieve the data and understand it.
Approaches to Text Data Mining
Given below are the approaches to text data mining:
1. Document Classification
Whenever there are many documents, be it online or offline, this is the best way to identify the data needed. Automatic document classification helps to identify the data easily with few keywords. Wordnet or expert knowledge tools help to find the relation between the terms or clusters so that the automation is done easily. Also, this can be done by pairing attributes and values with each other and creating an entity-relationship of words though the recurrence of words in documents is uncommon.
One set of documents can be classified from another by using certain terms and associating them. Also, the association helps to recognize the patterns and the frequency of data in the given set of documents. Another method of document classification in common is document clustering, where the documents of similar sets are identified and clustered into similar groups, as discussed above. Certain rules can be formed for document classification as this is different and difficult when compared to other data. The documents will be huge, and the contexts will be different from each other.
2. Keyword-Based Classification
When there are certain words or phrases that occur in frequent intervals, it is good to form a relationship of the words so that a correlation can be formed. This helps to do the association mining of data and automate the process. Preprocessing of the data has to be done to avoid confusing words by parsing and identifying the terms. Algorithms can be formed to identify the common words, and the pattern can be identified easily. This automation process reduces human efforts and produces relevant results to confirm the data. Association mining is done based on the terms in the data as each recurring word are identified as a term or item.
Worldwide data is mostly unstructured, and here the importance of text data mining comes into the picture. Without collecting information from unstructured data, the collection of data itself becomes irrelevant. Storing a huge amount of data and collecting information from the data using traditional tools becomes a challenge, and here Text data mining helps to a huge extent.
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This is a guide to Text Data Mining. Here we discuss the introduction, types, and approaches in text data mining, respectively. You may also have a look at the following articles to learn more –
