Introduction As part of our blog series, ‘Text Analysis 101: a basic understanding for business users’, we will aim to explain how Text Analysis and Natural Language Processing works from a non-technical point of view. For the first installment, we are going to discover how text is understood by machines, what methods are used in text analysis and why Entity and Concept extraction techniques are so important in the process. Text Analysis Text Analysis refers to the process of retrieving high-quality information from text. Cyberduck download for mac os 10.4.11. It involves Information Retrieval (IR) processes and lexical analysis techniques to study word frequency, distributions, patterns and utilizes information extraction and association analysis to attempt to understand text. The main goal of Text Analysis as a practice is to turn text into data for further analysis, whether that is from a business intelligence, research, data analytics or investigative perspective. There are certain aspects of text, that can be identified with modern techniques, that allow machines to understand a document, article or piece of text.

Text analysis tools using linguistic criteria in 90+ languages: text mining, coocurrence, keyword extraction and more. The analysis takes into account the linguistic features of each language such as morphology or grammar and is suitable for various text analysis techniques.

Technological advancements, greater computing power and investment in research has meant Natural Language Processing techniques have evolved, performance has improved and adoption across the business world has grown dramatically with the Text Analytics market now, according to Alta Plana’s latest report having an estimated market value exceeding $2bn. Traditionally NLP techniques focused on words. These techniques relied on statistical algorithms to analyze and attempt to understand text. However, there has been a push in recent times to equip machines with the capabilities to not just analyze, but to “understand” text.

There are numerous approaches to the problem some being more popular and more accurate than others. Document Representation Models – Bag of Words and Bag of Concepts Traditionally, analysis systems were focused on words and they failed to identify concepts when attempting to understand text. The diagram below outlines how, as we move up the pyramid and consider concepts in our analysis, we move closer to machines extracting meaning from text. Bag of Words The model is a representation that has been traditionally used in NLP and IR. In this model, all grammar, sentence structure and word order can be disregarded and a piece of text, a document or a sentence can be seen or represented as a “bag of words”. The collection of words can then be analyzed using the for occurrences of certain words, in order to better understand the document, based on its most representative terms. While analyzing words is somewhat successful, a greater focus on concepts within text has proven to increase a machine’s overall understanding of text.

Bag of Concepts Looking beyond just the words on the surface of a document can provide context to improve a computers’ understanding of text. As demonstrated in the pyramid above, analyzing the words alone can be seen as a base level analysis while considering concepts as part of the analysis goes a step further to improve overall understanding. While a concept based approach may provide greater insight, by not relying on the words alone and considering concepts as part of the analysis process. Combining both the BoW and BoC approaches to understanding text, performance and accuracy can be greatly improved. This is especially true when we are dealing with a somewhat lesser known sample of text. You can read more about the Bag of Concepts approach here: To move towards more of a concept-based model of Text Analysis we need to be able to identify entities and concepts within a text. In order to understand how this is done it’s important to discuss, what entities and concepts are and how we identify and utilize them from an analysis point of view.

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