Text Analytics – Overview
- Text analytics is the process of deriving high-quality information from text.
- It automatically extracts information from different written resources like websites, books, emails, reviews, and articles.
- The 3 different perspectives of text analytics are information extraction, data mining, and Knowledge Discovery in the Databases process.
- Text analytics tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.
- Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics.
Text Analytics – Market Size
The global text analytics market was valued at USD 5.46 billion in 2020 and is expected to reach a value of USD 14.84 billion by 2026 at a CAGR of 17.35% over the forecast period (2021-2026).
- Text analytics tools are being increasingly used by organizations to aid their business-making process by offering actionable insights from various forms of text sources, such as client interaction, e-mails, blogs, product reviews, tweets, and center logs.
- The primary objective of text analytics is to accumulate different forms of data, including structured and unstructured, which are further utilized for the analysis, thereby fuelling the organization’s business decisions.
Text Analytics – Advantages
- Enhance Customer Experience
- Data-Driven Decisions
- Enables Emerging Trends
- Prioritize Tasks/Issues
Text Analytics – 5 Best Software’s
- Amazon Comprehend
- Google Cloud NLP
- Monkey Learn
- IBM Watson
- Meaning Cloud
- Amazon Comprehend is a natural-language processing (NLP) service that uses machine learning to uncover valuable insights and connections in text.
- It uncovers valuable insights from the text in documents, customer support tickets, product reviews, emails, social media feeds, and more.
- It also simplifies document processing workflows by extracting text, key phrases, topics, sentiment, and more from documents such as insurance claims.
- Amazon Comprehend protects and controls who has access to your sensitive data by identifying and redacting Personally Identifiable Information (PII) from documents.
- Google machine learning derives insights from unstructured text.
- It also gets insightful text analysis with machine learning that extracts, analyses, and stores text.
- It is used to train high-quality machine learning custom models without a single line of code with AutoML.
- Google cloud NLP applies natural language understanding (NLU) to apps with Natural Language API.
- Monkey Learn makes it simple to clean, label and visualize customer feedback — all in one place.
- It is powered by cutting-edge Artificial Intelligence.
- It offers services like NPS/CSAT analysis, Review analysis, etc.
- IBM Watson analyzes text to extract metadata from content such as concepts, entities, keywords, categories, sentiment, emotion, relations, and semantic roles using natural language understanding.
- It can provide powerful insights by engaging with a full suite of advanced text analytics features to extract entities, relationships, keywords, semantic roles, and more.
- It Interprets text in thirteen different languages, with more on its way.
- It can identify high-level concepts that aren’t necessarily directly referenced in the text.
- Meaning Cloud is a Software as a Service product that enables users to embed text analytics and semantic processing in any application or system.
- It extends the concept of semantic API with a cloud-based framework that makes the integration of semantic processing into any system something close to a plug-and-play experience.
- It is available both in SaaS mode and on-premises.
