Text mining involves automatic extraction of high-quality information from written sources or text. It almost works in the same way as data mining part from the fact that text-mining tools are designed to handle unstructured data sets such as documents with full text. On the other hand, text analysis is structuring text data to make it machine readable. In other words, text analysis is the application of text mining in solving different problems. Text mining and analysis are utilized in several real-life fields including a systematic review of literature that can be used for necessary essay paper writing.
Application of Text Mining
Automatic correction and detection of errors or typos is the working mechanism behind all applications or software used for text mining. This could range from simple mistakes such as omission, duplication, and transposition to complex ones like original use of synonyms and spelling errors. This is particularly useful in college essay writing using spell checking systems or plagiarism checkers like Grammarly. Below is a breakdown of some of the applications of text mining:
- Document summarization: This can be applied in news summary where only the key points in a more significant original text document are provided
- Categorization of text: Here text is categorized for instance detection of explicit content or differentiating between non-spam emails and spam emails.
- Analysis of sentiments: It can be used to deduce customers’ perception of a company by extracting or detecting personal information from text documents.
- Entity/concept extraction: this can identify text entries of places, people and organizations into documents.
- Text Clustering: Large sets of data or documents can be organized automatically.
- Coreference resolution: this is applied in plagiarism checkers
Text analysis is done in several ways.
- Natural Language Processing: this is a type of machine learning technique that extracts meaning from plain text using computational methods.
- Machine Learning: machine learning, forms the basis of text analysis. Therefore, machine learning is an aspect of computer science in which a computer is trained to recognize trends and patterns.
- Network Analysis: network analysis aims to achieve the connection or link between concepts, modes of presenting, concepts and much more.
- Visualization: this is basically, the way in which data is seen regarding the relationships between different concepts.
- Topic Modeling: this is a form of machine learning in which themes and patterns are identified.
Favorite Tools used for Text Analysis
Almost everything in the current world is all about data or the information that you have. However, one needs to have the right tools for text mining or analysis to make meaning from astronomical data. The following are some of the best text analysis/mining tools.
SAS Text Miner
This software does not just extract valuable information from text content; it also discovers it using advanced linguistic models, statistical modeling and natural language processing. It also comes with tools for analytical and semantic modeling, which make it ideal for natural language processing, text analytics and as a taxonomy software.
This tool features template-based frameworks that are used in text analytics. Not only is it perfect for data mining, but RapidMiner is also essential in data evaluation, visualization, statistical modeling and preprocessing.
Natural Language Tool Kit
NLTK features several tools that are ideal for text processing, machine learning, and scikit-learn. For any text mining to be complete, some essential knowledge has to be done, and the NLTK has the appropriate tools for that.
Clarabridge is used in the provision of accurate text analytics by use of sentiment analysis and Natural Language Processing. It also features the Clarabridge Intelligence Platform that is powered by a text analytics engine that is essential in generating a report of customer feedback.
This tool is used to data scientists and research professionals regarding its effectiveness in entity extraction, concept extraction, sentiment analysis as well as categorization. Bitext is mainly, helpful in college essay writing since it incorporates 20 different languages and it captures dictionaries and grammars that are machine-readable.
Weka was initially used for agricultural domain data analysis. However, it has been upgraded, and now it comes with more sophisticated features such as algorithms for predictive modeling, data analysis, and visualization. As compared to other tools such as RapidMiner, it is free under General Public License. Additionally, it is capable of conducting some data mining tasks such as clustering, regression, classification among others.
This is another text-mining tool that is in the form of a text analytics engine. It is mostly utilized for sentiment analysis, enterprise search, market research, survey analysis, summarization among others. In addition to that, it supports multiple languages including Spanish, French, German, Portuguese and English. The tool also incorporates Salience, which is a text analytics engine that is multi-lingual. Moreover, Salience has text processing capabilities capable of conducting theme extraction, summarization, sentiment analysis and natural language processing.
This tool features Cogito, which is capable of performing text analytics, natural language search, natural language processing, data extraction, and categorization. In addition to that, the Cogito is also multilingual.
The tool converts raw customer feedback on social media and other channels into categorized information that is actionable. This includes sentiments, viewpoints, and topics, which are essential in decision-making. The categorized feedback makes a significant amount of text more understandable.
There you have it. These are some of the famous and useful tools that is used for text mining and analysis.