Text Analytics

1 min read

Text Analytics Software solutions extract high-quality structured data from unstructured text. Text analytics, also known as text mining, derives high-quality information from unstructured data sources in order to improve master data and produce new insights about products and services.

Scope

Text analytics solutions structure the input text, locate patterns within the structured data, and evaluate and interpret the output. Text analysis includes information extraction, lexical analysis to study word frequency distributions, concept/entity extraction, text categorization, document summarization, text clustering, pattern recognition, production of granular taxonomies, tagging/annotation, sentiment analysis, information retrieval, entity relation modeling, data mining techniques including link and association analysis, visualization, and predictive analytics.

Text Analytics
Text Analytics

Text analytics tools turn text into data for analysis through statistical, linguistic (natural language processing or NLP), and machine learning (ML) techniques and leverage advanced analytical methods to find concise meaning in huge volumes of text. These automated techniques extract knowledge, facts, and business insights from text. With text analytics, emails, online reviews, social media comments, tweets, call center agent notes, survey results, product reviews, and other types of written feedback and recorded interactions can be turned into valuable insights that cannot be easily measured otherwise.

Usage

Text analysis techniques are broadly used in a variety of verticals, including government, scientific research, bio medicine, legal, media, academics, and marketing. These solutions aid in records indexing and management. Text analysis software packages are also commercialized for security applications, such as analysis and monitoring of online news, blogs, and other plain text sources for national security and law enforcement purposes.

Text analytics and natural language processing solutions may require handling by data scientists—while the insights generated are often easy to understand, the actual technology is quite complicated and may require expert supervision. There are seven steps involved in text analysis; namely, language identification, tokenization, sentence breaking, part of speech tagging, chunking, syntax parsing, and sentence chaining.

Due to the emergence of Big Data, the need for AI technologies such as NLP and ML in text analysis is rapidly increasing. Text analytics solutions often feature sentiment analysis, which can recognize subjective material and extract various forms of attitudinal information such as sentiment, opinion, mood, and emotion. Text analytics is used to analyze sentiment at the entity, concept, or topic level and can differentiate between opinion holder and opinion object.

Challenges

Text analysis is often challenging because input data can be vague, inconsistent, and contradictory. Analysis is often further complicated due to differences in syntax and semantics, use of slang, sarcasm, regional dialects, and technical language specifications. As a result, text analysis needs a large amount of training and processing power to analyze such inconsistencies.

Takeaway

In conclusion, while text analytics is still improving, it does have numerous applications in its current iterations. Industries and governments are adopting text analytics solutions in order to gain insights into the mental workings of their customers and citizens. With text analytics, governments can improve law enforcement, while industries can modify existing products and services or create new ones to meet customer demand.

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