AI content detection tools look for stylistic traits and inconsistencies that can indicate whether an article was authored by humans or by an algorithm. They may also analyze word frequency, n-gram patterns, sentence length and more.

While they can be helpful, these tools are not foolproof and can be easily confused with human content. It’s best to use them as a supplementary verification method. Check out more at Identifying AI writing.

Classifiers

Classification, one of the most fundamental forms of holistic content recognition, tags an input document with a specific category. These categories can be product types, document topics, image colors, and more. When you identify a new piece of content as fitting into a particular category, you can quickly and easily search for it.

Manual text classification involves a human annotator interpreting the contents of each document and manually labeling them. While it can produce good results, this approach is labor intensive and time consuming. Automatic text classification applies machine learning and NLP to sort and recognize content in a faster, more cost-effective manner.

Levity offers a number of pre-trained, ready-to-use classifiers that you can use to automatically identify and categorize your content. You can also create custom trainable classifiers to more accurately match your content needs. However, the more you customize your classifier, the harder it will be to train and the more error-prone it will be.

Natural Language Processing (NLP)

NLP uses a set of rules to turn unstructured data into structured data that computers can understand. It enables automation of many tasks that require human input and helps to make search tools more effective.

Examples of NLP include sentiment analysis, which can identify the mood of incoming communications (like customer service chats or social media posts) in real-time, and text summarization, which automatically condenses large documents into a synopsis. It can also be used to perform language translation, which is especially useful in businesses with international operations.

Other NLP applications include automated tagging and classification of content, making it easier to organize and retrieve, and voice-activated search and commands. It can also be used for document redaction, which identifies and removes personal identifiable information from documents. NLP is key to transforming content operations across industries and enhancing user experience. The global AI software market, including NLP, is projected to reach 126 billion by 2025, demonstrating its significant value in the digital age.

Automated Text Analysis (ATA)

Using techniques like topic modeling, sentiment analysis, and named entity recognition, AI-powered text analysis software can quickly sift through thousands of documents to uncover hidden connections and generate insights. This efficiency enables researchers to focus on interpreting results instead of manual coding, enhancing the quality and scope of their studies.

Moreover, AI-powered text analysis enhances security protocols by monitoring communication and publication data to detect threats to public safety. For example, healthcare organizations use AI to analyze patient documentation and communication to identify unauthorized access or potential data breaches. This automated oversight bolsters compliance with industry regulations, safeguarding patients’ privacy and improving customer trust.

Attractions can also rely on AI-powered text analysis to examine large volumes of guest feedback, social media posts, and survey responses. This frees up staff time to spend on more urgent tasks. Moreover, AI-powered text analysis enables attractions to receive and process results in real-time so they can take immediate action to address guest concerns.

Text Mining

Whether it’s potential customers sending sales queries; employees submitting support tickets or responding to surveys, businesses are constantly receiving a deluge of unstructured text data. Manually processing and organizing this information is time-consuming, labor-intensive and susceptible to human error.

Text mining – or simply “text analytics” – uses machine learning to automatically analyze text-based data and find relevant information. It identifies themes, topics and words and extracts data like customer sentiment or market trends. Text analysis can also be used for tasks such as identifying product issues, predicting customer churn and detecting fraud.

For example, a wearable tech company could use a text mining model to scan online reviews and customer service chats to identify common product issues like battery life or connectivity problems. The model would then route the ticket to a team that can best address these concerns, improving the overall customer experience and helping reduce client frustration and churn. This also frees up employees to focus on higher-value tasks.