Natural language processing (NLP) is used by text analysis software, often known as text analytics or text mining software, to help users get insights from structured and unstructured text data. Sentiment analysis, key phrases, language, themes and patterns, and entities are examples of such insights.
Text analysis tools can ingest text data from various sources, including emails, phone transcripts, surveys, customer evaluations, and other documents. Businesses are better equipped to recognize and analyze consumer or employee sentiment, effectively classify documents, and improve written content by incorporating text data from these many sources.
What are the Common Features of Text Analysis Software?
- Language identification: Content analytics systems enable users to determine the language in which the text was written. This can be useful for determining the origin of a social media post or when a company has offices in multiple countries.
- Tagging of parts of speech: Once the language has been detected, text analysis software can tag each word with a part of speech, indicating whether the word is a noun, verb, or adjective, and so on.
- Syntax parsing: Syntax parsing is similar to part of speech tagging in that it helps break down how and why a sentence was created rather than understanding each word.
- Entity recognition: Text analytics systems can assist in determining not only elements of speech but also real entities. For example, a noun may be part of speech, but text analytics will evaluate whether that noun is a person or a place.
Check out this article to learn more about Text Analysis Software.
What Are the Advantages of Using Text Analysis Software?
- Sentiment analysis: Businesses constantly seek to evaluate client satisfaction, and text analytics is a simple way. Many text data sources, including social media, emails from consumers, phone transcripts, customer reviews, and others, can give customer attitudes. If a corporation understands its limitations or areas of strength with clients, it can better serve and manage those customers. This can eventually lead to more revenue.
- Staff satisfaction: Text analysis may be used to improve employee engagement and satisfaction, just as it can be used to understand customers better. While corporations should not necessarily spy on their employees, they can use surveys, emails, or phone transcripts to determine employee attitude and happiness. This can assist firms in ensuring that they are fostering the correct company culture and delivering a healthy and pleasant working environment.
- Survey analysis: Text analysis is frequently employed when businesses conduct surveys. These surveys could be for consumers or staff, but they could also be for market research. Swiftly extracting ideas verbatim from survey responses can provide firms with a distinct viewpoint and insight that multiple-choice questions may not deliver.
- Document classification: Document categorization is a simple use case for text analysis tools. Businesses frequently need to organize existing papers; extracting sentiment and themes can be considerably easier to categorize documents like invoices and contracts.
Here is the list of the best Text Analysis Software for your business.