Textual analysis is a study where any written documents get analyzed to find specific information. An analytic machine is used to scan articles or any other documents. From this scan, similar keywords or sentences in all documents can be highlighted and Machine learning text analysis can help you find very important information and see certain patterns from many different documents in seconds.
When analyzing documents, one must first break down the document into 7 core functions. By breaking it down in-depth research will be much easier. These seven functions are:Language Identification, Chunking, Sentence Breaking, Sentence Chaining, Part of Speech Tagging, Syntax Parsing, and Tokenization.
This may sound very obvious, but when it comes to text analysis the first thing anyone has to check is the language it is written in. Every language has its own set of rules so it is important to understand how languages are constructed.
Tokenization means to split the text into smaller units. From an entire document into paragraphs. From paragraphs into sentences and from sentences into words. Each one of the smaller units in its core is called tokens. These smaller units help with interpreting the words that are being read.
All sentences need to be broken down to make reading easier. No article or document can have one long ongoing sentence. This is why we use punctuation like a period or question mark.
English has eight types of part of speech: verb, adverb, noun, pronoun, adjective, conjunction, preposition, and interjection.The part of speech is an indicator of how the different words have different functions in the sentence.
Also read: Machine Learning in Python
When words are put together under certain phrases, it is known as chunking. The sentence ‘The beautiful girl runs slowly on the field,’ will be chunked into: (The beautiful girl)(runs slowly) (on the field).
How words are arranged in a sentence is known as syntax parsing. English uses the subject, verb, object(SVO) sentence structure. The syntax parsing is divided into two main categories:the noun phrase and the verb phrase structure.
Sentence chaining is when all off the sentences in the document follow up on each other. From these sentences one can easily determine the topic of what is being read.
Learn more about Machine Learning’s basics here: https://towardsdatascience.com/machine-learning-text-processing-1d5a2d638958.
Emails, posts, tweets, and all different sorts of data showed an enormous growth over the last couple of years. Analyzing all of these data can be overwhelming to many companies, especially those in marketing. The machines go through a process where large collections of documents are put together into a machine. It is done so new information can be discovered or help someone to answer questions from a survey. By using this method, a business can process data from 5000 or more surveys and find common key words or answers from the survey.
By using this machine one can also quickly put things into certain categories. For example, if 10 000 people used Uber and all of them gave reviews, this machine will automatically place these reviews under, positive, negative, or neutral.
In systematic literature one critically evaluates research to answer a question that was clearly formulated. This why you definitely need a tool that can organize all the answers into the categories needed. Read this to learn more about systematic literature and algorithms.
Which Text Analyzing Tools Are Available?
When it comes to deciding which text analyzing tool to use, here are the six best tools to choose from:
Regardless of which analyzing software you choose for your own personal use or your company; you can be sure you will get lots of worth for your money. Text analysis tools are not too expensive, easy to use, and it gives you accurate data so you can analyze data in a very short period.
Learning how to use a data process machine can be an efficient ability to have. With so many tools to choose from just make sure to choose the one according to your needs.