Text analytics has a wide array of applications. It can be used to arrange product reviews and apply shopping pattern recognition using keywords, topics, categories, tags, and annotation from text data. The software required to run the advanced analytic algorithms that predict, prescribe, and infer information from the text can be difficult to build into enterprise applications. But with Baseet, text analytics apps can be customized so that anyone may take advantage of this service – no data science background required!
What is Text Analysis?
Using linguistic, statistical, and machine learning techniques, text analytics applications take unorganized or otherwise senseless data and convert it into digestible and usable information.
Essentially, word cloud tools detect key words and phrases across languages that appear most often within a specific dataset. Text analytic tools have the capacity to process over 40,000 search queries every second, or 3.5 billion searches every day. Examples of text analytics include association analysis, visualization, information retrieval, and lexical analysis.
What Are Text Analytics Used For?
Text analytics help researchers understand public opinion without having to ask people directly. Businesses benefit from these features because of how valuable it is for extracting information from daily interactions. Enterprises can take text from web reviews, postings on Twitter and other social media profiles, information in emails and text messages, or survey responses and turn them into meaningful insights. Search histories can also be used to observe patterns and track global trends.
Text analytics is an incredibly useful tool for business intelligence. Enterprises and researchers can use text analytics to track, record, and analyze the world of information from people’s casual interactions online. Consider one example from the realm of foodies. Using photographs of delightful dishes and scrumptious snacks, researches can gather information about what people eat, how nutrition affects health, food borne illnesses, cultural differences, and other food related habits.
The text analytics applications for consumer-driven industries is massive, but the opportunities in this space are endless. Text analysis has even helped save lives during disaster recovery by assessing public needs during and after disasters, improving readiness to respond and support.
Building Text Analytics Apps With Baseet.ai
Baseet.ai provides a pre-built app that helps users to connect with Azure Text Analytics service that analyzes input text, detects its language, identifies the sentiment of the text, extracts the key phrases, and recognizes the entities.
Baseet has pre-built nodes that can be linked together through our easy-to-use visual interface. A “node” is a reusable, customizable component that can be connected with other nodes in an easy drag-and-drop format to make increasingly complex and capable apps. An app is a series of nodes that can be linked together to make a functional application. If you’re looking to add Azure’s Text Analytics Service to your existing code, simply take our auto-generated API and embed it into your app.
Azure provides the main text analysis AI service in the natural language processing field, and a sentiment score is used to measure the overall positivity/negativity in the text. It also extracts the key phrases from the text based on phrases and keywords, which helps with tasks like summarizing and indexing data. With this approach, developers can build solutions around existing services with ease. Here’s how:
1. Setup API
- If you don’t have an Azure subscription, create a free account before you begin. Then to get a Text Analytics API subscription key, you need to follow the steps mentioned in this link or get a free trial subscription key from here.
2. Access Baseet’s Visual Application Builder
To build an app with Azure Text Analytics, login to your Baseet.ai account. If you don’t have an account yet create a free one here.
From there, all you need to do is create a new application. To do this, simply open the app editor, and from the right-side panel “node search” list, search for “Azure-Text Analytics.” This node helps users to connect directly with the text analytics service from Azure. Once you’ve found the node, just drag and drop it into your programming:
3. Select Inputs
Either use our drag-and-drop editor or code inputs yourself:
- Text Analytics API Subscription Key: credential key you got from Azure API for text analytics service.
- Text Analytics API Endpoint URL: endpoint url you got from Azure API for text analytics service.
- Input Text: the text you want to analyze.
4. Generate Outputs
Connect Baseet’s pre-built nodes with your inputs, run the application, and see your outputs:
- PDF_URL: URL of the generated PDF file that contains the analysis results.
Need More Information? Check Out Our Video Walkthrough and Sample App
Baseet is built to make accessing the power of AI easy, and we’re always happy to help! This video shows you each of the steps described above – follow along, and you’ll have a text analytics app up and running in no time!
If you want to dig even deeper, Baseet.ai provides sample apps for beginners to show how to use specific nodes. For the Azure Text Analytics service, follow Azure – Text Analytics APP to show the usage for each node.
The output is a URL for the PDF file, open it:
The generated PDF analytics file will be shown as follows:
The results of the Azure Text Analytics response are gathered and formatted as HTML and passed to the PDF generator node.
Baseet’s visual UI has eliminated the need for the complexities of sophisticated data science skills. This saves businesses looking to capture the benefits of text analytics real time and money while also greatly reducing the likelihood of error. Try it for yourself by signing up for a free account at https://console.baseet.ai/signup or visit us on the web at https://baseet.ai/. Also, stay up-to-date on what our dynamic and user-friendly platform has to offer by following us on social media at @BaseetAI on Twitter, Facebook, or LinkedIn.