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What To Look For in Text Analysis Software

Whether it’s uncovering code or trying to understand trends, text analytics has become a popular tool to utilize. Text analytics combines a set of machine learning, statistical, and linguistic techniques to process large volumes of text with no predefined formats to derive patterns. This enables businesses, governments, researchers, and more to comb through content to help make crucial decisions. Here is a little more about how text analysis can be used and how to find the best software for your situation.

How can text analysis be used?

When looking for the best text analysis software, it’s important to understand the handful of techniques that go into analyzing unstructured text. Sentiment analysis is used to identify the emotions conveyed by this text such as product reviews, social media posts, and blogs. This helps to quickly identify positive or negative sentiments. There is also topic modeling, which finds major themes or topics in a massive volume of documents to identify keywords. This is usually turned to by researchers and media outlets to uncover details in a review faster than ever before.

Named entity recognition, or NER, is a text analytics technique used to identify named entities, specifically nouns. This is used for information retrieval by search engines and to classify content. Event extraction is the technique by which moments are recognized in text content. This is commonly used in the business realm to identify key events like mergers and acquisitions, even important meetings. Term frequency-inverse document frequency, or TD-IDF, is used to determine how often a term appears in large text or in a group of documents. This determines its importance, which is helpful when combing through lengthy text such as legal documents.

What are the steps involved in text analytics?

Text analytics is a sophisticated technique that involves several pre-steps to make sure that any tools and software utilized work at their most efficient. A model workflow starts with data gathering. Text data is often scattered about, so this takes time to bring together all functions, key phrases, and customer sentiment into a database for analysis.

This is followed by data preparation. Once the unstructured text data is available, it needs to go through preparation for machine learning algorithms to analyze it. In most text analytics software, this usually happens automatically. There are several techniques that can rely on natural language processing, such as tokenization, which breaks down words into smaller units. Part-of-speech tagging assigns grammatical categories to words. There’s also parsing, the process of understanding syntactical structure. Lemmatization removes suffixes and affixes, while stop-word removal eliminates frequently used words of no relevance, such as “and” or “the.” After the preparation phase, text analytics can then be performed to derive insights.

What are the benefits of text analysis?

With an understanding of what text analysis software can do, you can now focus on the benefits of these incredible tools. A text analysis platform can help businesses to understand customer trends quicker than ever before. By gaining this knowledge from customer surveys and reviews, organizations can afford quicker decision-making, enhancing business intelligence, increasing productivity, and leading to more savings.

Text analysis tools can also help researchers extract matters relevant to the task at hand, saving time for breakthroughs and insights that would take far longer for deeper analysis by humans. This software also helps garner an understanding of general trends, enabling users to address their specific needs with better decisions. The good news is that there are plenty of text analysis software solutions on the market that can meet the specifications of your organization. Exploring the options can lead to answers to open-ended questions and bring unprecedented flexibility to businesses of any size.

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