Free Text Analyzer – Flesch Reading Ease And More – 7 Languages!

Free Text Analyzer (Explanation):

Text Analyzer is a free tool to calculate statistics from text. It helps determine readability, complexity, and grade level.

Tool supports 7 languages:

• English
• Polish
• Spanish
• French
• German
• Italian
• Dutch

Due to the fact that each language is different and specific, therefore different types of scores are available for one language and not for another.

The tool is completely free to use! 🙂

Scores Types

Text Analyzer is able to calculates multiple scores for seven languages. Please find the following table which describes which scores are calculated for each language (X means is calculated, empty – not calculated):

Scores Description

In the following sections you can find what the individual scores mean and how to interpret them. To make it easier I have created the tables to sort it out.

A text analyzer is a tool or program that performs analysis and processing of written language. There are many different types of text analysis, including sentiment analysis, which involves identifying the sentiment or emotion expressed in a piece of text, and topic modeling, which involves identifying the main topics or themes discussed in a piece of text. Text analyzers can be used for a variety of purposes, such as analyzing customer feedback, monitoring social media, or analyzing text data for research purposes. There are many different text analysis tools and approaches available, ranging from simple keyword counts to more advanced natural language processing techniques.

What Is Text Analysis?

Text analysis is a process of analyzing and interpreting written or spoken language to extract and identify meaningful patterns and insights. It can be used to understand the sentiment, emotion, and underlying meaning of a piece of text.

There are many different techniques and approaches to text analysis, ranging from simple techniques like counting the frequency of words or phrases, to more complex techniques like natural language processing (NLP) and machine learning". Some common applications of text analysis include sentiment analysis, content analysis, topic modeling, and named entity recognition.

Text analysis can be used for a wide range of purposes, such as understanding customer feedback, analyzing social media posts, or conducting research in the social sciences. It can be done manually, or with the help of specialized software tools and algorithms.

Why Text Analysis Is Important?

Text analysis is important because it allows you to extract valuable insights and information from large amounts of text data. It can help you understand the sentiment, opinions, and emotions of people, as well as identify patterns and trends in the data.

Some specific applications of text analysis include:

1. Sentiment analysis: This involves analyzing the sentiment or emotion expressed in a piece of text, such as whether it is positive, negative, or neutral. This can be useful for customer service, marketing, and public opinion research.
2. Opinion mining: This involves extracting and summarizing the opinions or views expressed in a piece of text. This can be useful for understanding consumer preferences and for product or service improvement.
3. Content analysis: This involves analyzing the content or meaning of a piece of text to extract important themes and topics. This can be useful for research, journalism, and market analysis.
4. Entity recognition: This involves identifying and extracting named entities (such as people, organizations, and locations) from a piece of text. This can be useful for information retrieval and database" management.

In general, text analysis can help you gain a deeper understanding of the data you are working with, and can be a powerful tool for making data-driven decisions.

Flesch Reading Ease

The Flesch-Kincaid readability tests are used to determine how difficult a piece in English is to grasp. The Flesch Reading-Ease and Flesch-Kincaid Grade Level exams are available. Although they employ the same underlying criteria (word length and sentence length), the weighting variables are different.

Flesch Kincaid Grade

These reading exams are widely utilized in the educational profession. The “Flesch-Kincaid Grade Level Formula” displays a score as a grade level in the United States, making it easier for teachers, parents, librarians, and others to determine the readability level of various books and texts. It can also refer to the number of years of schooling typically necessary to comprehend this literature, which is important when the calculation yields a result larger than 10.

Automated Readability Index

The automated readability index (ARI) is a readability test for English texts that is used to determine a text’s understandability. It produces an approximate estimate of the US grade level required to grasp the material, similar to the Flesch-Kincaid grade level, Gunning fog index, SMOG index, Fry readability formula, and Coleman-Liau index.

Dale Chall Readability

The Dale-Chall readability formula is a readability test that offers a numerical measure of the difficulty that readers have when reading a document. It use a list of 3000 words that groups of fourth-grade American pupils can consistently grasp, with any word not on that list considered tough.

Gunning Fog

The Gunning fog index is a readability measure for English text in linguistics. The index calculates the number of years of formal education required to grasp the material on the first reading. For example, a fog index of 12 necessitates the reading ability of a senior in high school in the United States (around 18 years old). Robert Gunning, an American businessman who had previously worked in newspaper and textbook publishing, created the exam in 1952.

Gulpease Index

The Gulpease Index is an index of readability of a text calibrated on the Italian language. Compared to others, it has the advantage of using the length of the words in letters rather than in syllables, simplifying the automatic calculation.

Defined in 1988 as part of the research of the GULP (University Linguistic Pedagogical Group) at the Seminary of Educational Sciences of the University of Rome “La Sapienza”, it is based on surveys collected between 1986 and 1987 by the chairs of Philosophy of Language and Pedagogy of the Institute of Philosophy.

The Gulpease index considers two linguistic variables: the length of the word and the length of the sentence with respect to the number of letters.

Osman

Specific Scores Only For Spanish Language

Fernandez Huerta

Readability is the linguistic readability of the text, that is, whether it is easy or difficult to understand. It does not cover typographical aspects that greatly influence the ease of reading.

José Fernández Huerta created the second formula to measure the readability of texts in Spanish in 1959 . It is based on that of Flesch (for English).

Szigriszt Pazos

In 1993, the journalist Francisco Szigriszt-Pazos, proposed in his doctoral thesis a formula to measure readability (easy reading comprehension of the text). It is an adaptation to Spanish of the Flesch equation, designed for English.

Additional Scores

• Smog Index – The SMOG grade is a readability metric that assesses the number of years of education required to comprehend a piece of writing. SMOG stands for “Simple Measure of Gobbledygook.” SMOG is frequently used, especially for verifying health messages. The SMOG grade has a 0.985 correlation with a standard error of 1.5159 grades with the grades of readers who understood the test materials completely.
• Coleman Liau Index – The Coleman-Liau index is a readability test developed by Meri Coleman and T. L. Liau to assess text comprehension. Its outcome, like the Flesch-Kincaid Grade Level, Gunning fog index, SMOG index, and Automated Readability Index, approximates the grade level believed essential to grasp the text in the United States.
• Linsear Write Formula – Linsear Write is an English text readability measure that was allegedly designed for the United States Air Force to assist them calculate the readability of their technical manuals. It is one of several such readability measures, but it is especially developed to compute the grade level of a text sample in the United States based on sentence length and the number of words with three or more syllables.
• Text Standard
• Gutierrez Polini – With the purpose of measuring the comprehensibility of a text, Luisa Elena Gutiérrez de Polini (1972) created the first formula conceived, from the beginning, for Spanish, that is, it is not an adaptation of another for another language.
• Crawford – It is used to calculate the years of schooling needed to understand a text. It was devised by Alan N. Crawford in 1989. Only valid for elementary school children

Could You Please Share This Post? 
I appreciate It And Thank YOU! :)
Have A Nice Day!