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The term big data became popular in the 1990s to describe data sets that are not only big in volume, with terabytes at the minimum, but also big in velocity and variety. However, research has shown that business value is derived from having not only big data, but the right data. Data is seldom usable in its raw form. Even structured data has to be processed into relational databases to be used by systems to provide practical information. Unfortunately, more than 80 percent of data generated today is considered unstructured, which requires different processes, methods, and tools. One way to derive useful information from unstructured data is with a text analysis tool.
What is text analysis software?
Text analysis is the process of parsing texts from unformatted content and unstructured data to convert them to machine-readable data. Text analysis software is software that automates the process of parsing, retrieving, sorting, manipulating, and understanding unstructured data in a practical manner. The software uses advanced technology such as artificial intelligence (AI), machine learning (ML), and other analytical tools. It can be used to help businesses derive valuable information, discover patterns, and manage, use, and reuse content.
Benefits of text analysis software
Today, data comes in various formats such as through images, audio, video, and text. A text analysis tool can organize unstructured text data and prepare it for machine learning analysis. Text analysis of unstructured data coming from social media, surveys, news reports, and online reviews, for example, can provide companies the insight to make data-driven decisions such as:
Text analysis, text mining, and text analytics are similar but not identical terms. Text analysis software aims to address the ambiguity of the human language. The software solutions can extract data from various sources to channel and classify it using different techniques such as word frequency, collocation or co-occurrence, concordance, clustering, and word sense disambiguation. For text classification techniques, it can use sentiment analysis, topic analysis, or intent detection. Text analysis and extraction can be performed simultaneously to extract specific keywords or entities such as people, companies, or locations. Here is a list of highly recommended text analysis tools in no particular order.
RapidMiner offers a data science platform that includes several tools, including text mining and text analysis tool. Unstructured data from various content sources such as online reviews, social media chatter, call center transcriptions, and claim forms are analyzed to extract information and insight. The insight can then be used to improve marketing, product development, and risk management. The software is comprehensive and easy to use for all skill levels, with features such as automation, data cleansing and transformation, algorithm selection and validation, and visual model operations.
Thematic provides customer feedback analysis by unifying all text feedback from various channels and automating analysis. Its text analytics software can capture the meaning in individual phrases and group similar phrases into themes. The text analysis software can uncover unknown themes in customer feedback and highlight trends and fluctuations. Other features include workflow templates for soliciting customer feedback, sentiment analysis to gauge themes, customizable themes editor, and built-in Google Translate API.
The Lexalytics Intelligence Platform is a data analytics platform with natural language processing (NLP) and text analytics features. The solution is applicable to various industries. It can process high volumes of text data and be deployed on-premise, running behind a firewall, or on a private cloud. The software breaks apart sentences and phrases using NLP to evaluate semantics, syntax, and context to perform sentiment analysis, categorization, named entity recognition, theme extraction, intention detection, and summarization.
MeaningCloud is a text mining and text analytics solution. It is an easy-to-use and affordable solution for extracting information from various kinds of unstructured content such as social conversation, articles, and documents. s include integrations and APIs. For example, it has add-ons for Excel, Google Sheets, RapidMinder, and Zapier so that text analysis features can be used inside these applications. It also has cloud APIs that developers can use to add semantic analysis capabilities to their apps through web services, SDKs, and plug-ins.
Ontotext helps enterprises make use of their proprietary information using AI and global knowledge for accurate data interpretation. They link diverse data, enrich it with text analytics, and index it in GraphDB to create big knowledge graphs. The Ontotext platform looks at data, semantic metadata, and content, then uses ML, ontologies, taxonomies, and rules to create knowledge graphs. It is an effective semantic tagging solution that analyzes text, extracts concepts, and identifies topics, keywords, and important text relationships.
MonkeyLearn is a text analysis software that can be used by support teams, product teams, and developers. It offers an all-in-one text analysis and data visualization tool, APIs, and word cloud generators. The software can automatically tag tickets and route them based on text data. It can also provide insights coming from customer conversations, product reviews, and surveys to help product developers build better products and customer experience.
IBM Watson Discovery
IBM Watson Discovery is an AI-powered search and text analytics system that uses NLP to find answers in content and uncover business insights from documents, web pages, and big data. Watson Discovery uses semantic search to add context to answers by inspecting content across connected data sources, pinpointing the most relevant passage, and providing the source document or web page. It can find hidden patterns, trends, and relationships between different pieces of content. The software can also help create smarter chatbots.
Azure Text Analytics
Microsoft Azure’s Text Analytics is an AI-powered service that can uncover insights in unstructured text. Using NLP, the text analysis tool can identify key phrases and entities such as people, places, and organizations to help users understand common topics and trends. The solution includes domain-specific, pre-trained models that can evaluate text in a wide range of languages. Other features include broad entity extraction, sentiment analysis, and flexible deployment, whether in the cloud, on-premise, or at the edge in containers.
Amazon Comprehend offers text analysis solutions using NLP and ML to help users discover insights and relationships in text. It uses ML to go through customer emails, support tickets, product reviews, social media, and advertising to analyze customer sentiment. The service can extract key phrases, places, people, brands, and events, analyze if it is used positively or negatively, and organize a collection of texts by topic. It can be a solution for call center analytics, to index and search product reviews, to personalize content on a website, or for customer support ticket handling.
Google Cloud Natural Language
Natural Language helps users derive insights from unstructured text using Google’s ML. It allows users to extract information about people, places, and events. ML reveals the structure and meaning of text data that aids in understanding social media sentiment and customer conversations. It includes ML custom models to classify, extract, and detect sentiment, APIs that can be integrated to apps, and real-time analysis of unstructured medical text.
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Jose Santos is a long-time contributing writer for project-management.com. He is a subject matter expert in the field of project management and has many years of experience writing about project management software and tools. He has written hundreds of articles for the project management industry, including numerous software reviews, book reviews, training site reviews, and more.