Using text analysis API can be a transformational decision for your business. This article will cover the prominent use cases of using a text analytic APIs. In addition, to use cases, we will also give real-world examples of where these tools can be used and how they can be leveraged to create value for a company, team, or application. This article covers text analytics APIs: text categorization, sentiment analysis, text prediction, and translator API. The choice behind these APIs came because of the value added each brings to the table. In other words, these text analytics APIs present the highest business value in potential and use cases.
Text Categorization API
One of the primary use cases of text analytics is the extraction of text categories. This feature enables companies, software developers, and other stakeholders to understand text content easily. We start by explaining how this feature works to understand this use case. A text categorization API provides the ability to take a text and classify it into a specific category. To elaborate, this API works by training a machine learning model to classify a chunk of text based on keywords, sequences, and tokens. Once the machine learning model is trained, the next step is classifying the text content into different categories. Indeed, this step eliminates human review, reduces time-intensive tasks, and automates text categorization.
The text categorization API provides enormous potential by enabling businesses and companies to understand customer requests easily. One of the most accessible examples of this feature is the usage of this API in customer support. While creating chatbots, serving customers and addressing their issues and concerns can be time-consuming. It is both time and resources consuming to hire a team and train them to answer questions from your customers. To decrease the intensity of customer support resources and the human resources needed to support clients, companies can use this API. This usage can help firms categorize customer questions and send the customer directly to a knowledge base that contains the answer to the problem or issue they are facing.
Sentiment Analysis API
Another interesting use case of text analytics is sentiment analysis. The software engineering communities commonly use this feature to analyze social media data. The analysis process helps understand community feelings and perceptions about a product or service based on their expressions and sentences. The sentiment analysis operations performed in text analysis combine classification and natural language processing. To simplify these concepts, we will explain the process of transforming a standard text into a sentiment result. Initially, we will have a text that should be cleaned from all punctuations. This step is essential because the text analytics API will sometimes not support cleaning the text, and this step will have to be done by the developer. Once the text is clean and ready, we send the expression, sentence, or statement using an API request. Once summoned, this request will call a machine learning model that will take the text we sent and classify it as either positive, negative, or neutral. It is critical to understand that a text can easily be classified by calculating the number of positive, negative, or neutral words and finally come up with a classification based on the most frequent sentiment in each sentence.
Text Prediction API
Text prediction APIs fall under the deep learning category of machine learning models. Indeed, these tools enable the prediction of a keyword or a set of words based on a context. This task seems complicated at first, but thanks to the latest deep learning algorithm, it can be accomplished and performed well. This feature uses a set of keywords and predicts the next word based on a trained text. The computational logic of this feature relies heavily on probabilistic prediction. To explain, if a set of words is used in a sentence, the deep learning algorithm remembers these keywords and will produce the same results if a user uses these keywords.
The text prediction API seems to provide companies with a powerful tool to create advanced chatbots. These bots can generate a harmonic answer and help customers navigate goods or services online. In addition, this API can also automate the creation of audio responses that can be used to answer customers in case of an audio call or audio interaction. Before the invention of text prediction models, most online chatbot interactions seemed robotic and lacked a human-friendly outlook. Thanks to the text prediction API, it is less likely for a customer to ask for a human during an online exchange if the conversation is smooth and friendly.
The translator API opens the door for many companies to reach the international market. In other words, it enables companies that are already established to translate business content to a local language in case of expansion. The text API presents a straightforward infrastructure. Initially, the user sends via the API an original language and requests the translation to the intended language. The API is a time saver because it can automate the translation of the company’s social media pages, website content, and marketing emails, all with a simple API request.
An additional use case of the translator API is the customer interaction scenario. If an international customer requests assistance, the company will not be obliged to find a customer support agent that speaks the local language of that given customer. Using the API translator in this scenario makes it easy for the company to create a single language knowledge-based system that can be used to answer customer questions in their preferred language.
Text analytics APIs are becoming increasingly cost-effective for automating some of the day-to-day business processes. Creating most of the features we covered in this article is more costly for firms. An easily accessible set of text analytics tools via an API is the most advanced way of benefiting from AI advancement without spending vast resources. This article covered the underlying potential of using these APIs and showcased their powerful attributes using business examples.