Text analytics, also known as “text mining,” automates what people — researchers, writers, and all seekers of knowledge through the written word—have been doing for years[i]. Thanks to the power of the computer and advancements in the field of Natural Language Processing (NLP), interested parties can tap into the enormous amount of text-based data that is electronically archived, mining it for analysis and insight. In execution, text analytics involves a progression of linguistic and statistical techniques that identify concepts and patterns. When properly tuned, text analytics systems can efficiently extract meaning and relationships from large volumes of information.
To some degree, one can think of the process of text analytics as the evolution of the simple internet search function we use every day, but with added layers of complexity. Searching and ranking words, or even small phrases, is a relatively simple task. Extracting information from large combinations of words and punctuation — which may include elements of slang, humor, or sarcasm — is significantly more difficult. Still, text mining systems generally employ a number of layered techniques to extract meaningful units of information from unstructured text, including:
- Word/Phrase Search Frequency Ranking – What words or “n-grams” appear most often.
- Tokenization – Identification of distinct elements within a text.
- Stemming – Identifying variants of word bases created by conjugation, case, pluralization, etc.
- POS (Part of Speech) Tagging – Specifically identifying parts of speech.
- Lexical Analysis – Reduction and statistical analysis of text and the words and multi-word terms it contains.
- Syntactic Analysis – Evaluation of sequences of language elements, from words and punctuation, and ultimately mapping natural language into a set of grammatical patterns.
The purpose for all of this NLP processing is to compare those computational nuggets with classifications or “codings,” that trained experts have assigned to representative text samples. Interpreting language is an exceedingly complex endeavor, and one that computers and software cannot effectively do without being “trained.” As such, text classification systems are designed to compare human codings with the patterns that emerge from computational analysis, and then mimic the expert coders for all future input.
As you may expect, the quality of any custom text analysis system is largely determined by the quality of the human coders it is trained on. As such, strict rules must be enforced on the human coders, with the knowledge that software classification systems are very literal (think “Dr. Spock”). Still, once effective coding rules are established that result in discernible patterns, text analysis systems are incredibly fast and consistent. Advanced classification systems, like the one employed by Mediate Metrics, are also adaptive, constantly evolving with the ebb and flow of political issues and rhetoric.
- The Year of Text Analytics (customerthink.com)