Analytics has become an almost inevitable part of business operations within the era of digital transformation. This includes text analytics, combining machine learning and other linguistic techniques to process large volumes of unstructured text to uncover insights and patterns with great efficiency. Text analytics has become more commonplace in the world in recent years, often interchangeable with the process of text mining. Here are some just of examples of text analytics available.
Examples of Text Analysis
There are several techniques related to the analysis of an unformatted text. Among the leading text analysis examples is sentiment analysis. This is used to identify emotions conveyed by the unstructured text. This method of analysis includes polarity analysis, which is used to uncover positive or negative sentiment across customer interactions like social media posts and product reviews. The categorization technique is used for a deeper dive, trying to convey if the text is meant to come off as disappointed, angry, or any other emotion. This helps uncover audience trends and prioritize customer service issues based on severity.
There’s also topic modeling, finding major themes in a massive volume of text. This type of analysis relies on keywords to extract important insights. Researchers rely on this to uncover subjects of text, sometimes working in conjunction with term frequency analysis to find the importance of that word in a document. There’s also event extraction, a text analysis that operates based on location and linking senders, often relied upon for national security and public safety purposes. Lastly, named entity recognition (NER), which is a key entity for search engines by identifies the value of nouns being searched for in a given query.
Steps of Analysis
Text analytic software has a variety of different features, but there is very much a protocol to going through the process to uncover unfounded information from everything from significant legal documents to just an email. This all starts with data gathering, as external information needs to be gathered for analysis. Once the unstructured text is available, data preparation begins. It goes through several steps before machine learning algorithms can analyze it.
There’s tokenization, where algorithms break the continuous string of text data into smaller units that make up entire words or phrases, operating as the basis of natural language processing. That’s followed by part-of-speech tagging, each token in the data being assigned a grammatical category. Next up, is parsing, the process of understanding syntax in the text through either dependency or constituency. Then, there’s lemmatization and stemming, removing suffixes and affixes associated with the tokens. Finally, there is stop-word removal, the phase when all of the tokens that have frequent occurrence are removed from text analytics, such as “and” or “the.”
After the preparation of unstructured text data, text analytics techniques can now be performed to derive insights. Prominent among them are text classification and text extraction. Text classification, also known as text categorization or tagging, assigns certain tags to the text based on its meaning. This analyzes customer reviews based on positives and negatives. Classification often is done using machine learning-based systems. These systems take past examples or training data to assign tags to new datasets of any volume.
Text extraction uncovers recognizable and structured information from the unstructured input text. This information includes keywords, names of people, places, and events. One of the simple methods of extraction is regular expressions. However, this is a complicated method to maintain as input data increases. However, it’s an effective way to extract vital information from unstructured text that a human reader just might not see. These functions and useful insights are helping organizations across all industry realms deal with urgent issues and embrace new opportunities for the long run.