10 Essential Features Of A Text Analytics API
Text analytics features define how well a text analysis API will function. In order to draw intelligent insights from a wide range of data, you need an application programming interface (API) that has certain critical features. These features help the API determine and extract meaningful information from documents. All you have to do is plug and play.
So let’s get down to the basics and understand what these essential features of a text analytics API are.
What Are The Most Important Features Of A Text Analytics API?
There are 10 essential text analytics features that a text mining API must have in order to give you the most accurate results. These include neural networks for data classification, natural language processing, semantic clustering, and others. Let’s read about them in detail.
1. Topic, Feature, Aspect Classification:
Your text analytics API must have the capability to derive topic, feature, and aspect classifications from terabytes of data. It does this through the use of Neural Networks, which are a set of machine learning algorithms that detect hidden patterns in sequential data as in text documents. Thus, when analyzing data, the API categorizes words and phrases under aspects or features such as price, convenience, food, etc for more accurate data mining.
Neural Networks remember these data patterns and learn to apply them to future scenarios, similar to how a human brain understands past information to remember cause and effect. This is also what makes the API become smarter over time as it processes more and more data.
2. Semantic clustering:
Machine learning (ML) algorithms understand semantics in languages and cluster phrases and words accordingly so that there are no redundancies. This helps in ensuring that text analytics results are precise and you do not get false negatives or positives in sentiment scores.
3. Natural language processing
Natural language processing (NLP) is one of the most essential text analytics features. This machine learning task identifies the language and understands grammatical rules, sentence phrases, punctuations, colloquial expressions, and the like, in order to make sense of the data.
The NLP capability enables the API to read a wide range of documents in the same manner as a human would in order to derive intelligent inferences from them. Whether it is from Youtube video analysis, or from a company’s CRM data from software like Hubspot, the API can process both kinds of data through NLP.
4. Native multilingual ability
Another one of the key text analytics features, especially if you need analysis of text in multiple languages, is the ability of the API to read each language natively. It can do so with the help of a part-of-speech tagger for each language. This is important because each language follows its own grammatical rules and has unique colloquial usage, which is very common when analyzing text from surveys, reviews, or social media comments where people mostly write in an informal manner.
Machine translations do not take into account the individuality of a language and process all languages in English, which means that they leave out nuances in comments, which may contain important information. When aggregated, this leads to lower accuracy and efficiency.
5. Named Entity Recognition
Named Entity Recognition (NER) enables the API to read data and recognize important entities such as popular places, brands, currencies, people, geographical locations, and other “named” entities. Thus, for example, when analyzing sentiment in social media listening data, this is how the tool will pick out relevant information such as the name of a celebrity, popular attraction like the Eiffel tower, or an airport.
6. Knowledge Graph
A Knowledge graph is one of the ten key text analytics features that an API needs to have for text analysis as this is what makes it infer insights from data. A knowledge graph allows it to remember past information through neural networks and create a map. For example, the API’s NER capability will detect the word Met and understand that the text is talking about the Metropolitan Museum of Art.
Now the Neural Network would have stored information that the Met is in New York, and this will enable the text analytics tool to infer that the comment is referring to a place in New York, which is in the United States. This intricate mapping of information to derive information that is not explicitly mentioned in the data is possible because of knowledge graphs. Therefore, they are very useful for high-accuracy data mining.
7. Sentiment analysis
The text analytics API needs to have sentiment analysis capabilities so that you can use it for important customer experience analysis or for brand awareness strategies. The ability to mine emotion from data makes sentiment analysis one of the must-have text analysis features.
This feature allows the tool to be used in almost any setting, including for human resource functions such as extracting employee experience insights or in healthcare settings to ensure that patients are having a satisfactory experience at a health facility.
Having a text analytics API is worthwhile only if it can give the most accurate interpretation of the data it is analyzing. The accuracy of the API is highly dependent on the rest of its key features including NER, NLP, Neural Networks, knowledge graphs, and others. The API must be first trained to fit your business requirements, for which it usually takes two or three iterations, with adjustments made during each phase. After that, the API should easily have at least an 85% accuracy rate.
9. Speed & Scalability
The speed and scalability of the text analytics API are of paramount importance. This feature ensures that the API is good for use even if your data scales and you need high-speed results for your business. This depends a great deal on the language that is used to build the API. Tools built for sentiment analysis are mostly developed using Go or Python, both of which are excellent languages. However, text analytics and sentiment analysis APIs built on Go are better for virtualized, Cloud-based environments. Read more.
10. Flexible installation
The flexibility of installation is one of the many essential text analytics features a data mining tool must have because you should not have to use a Cloud environment if you do not want to, and vice versa. Flexible installation gives you the choice of having the API integrated on-premise behind your firewall for added security, such as is often needed in banking or healthcare, or on Cloud, if that’s what you prefer. A low-code flexible API integration gives you peace of mind knowing that your data is safe and secure.
What Are The Business Applications of Text Mining?
Text analytics is an essential part of a company’s marketing strategy because it can give important information about customer behavior, employee experience, social media engagement, brand reputation, and more, as below.
1. Customer experience analysis
Text analytics features help decode customer behavior through analysis of customer experience data that may be in the form of social media comments, reviews, survey responses, etc.
2. Patient experience analysis
Hospitals, clinics, and healthcare facilities can all extract valuable information from patient experience data to improve facilities and provide a better patient experience.
3. Employee experience analysis
Companies can mine employee feedback data to understand employee motivations to build a more humanized work environment.
4. Search engine optimization
You can identify important information in terabytes of data in your data repository through search engine optimization.
5. Social media listening
You can build better engagement with your target audience and extract important competitor and market insights.
6. Detect data patterns
A text analytics API can help you detect and understand data patterns, which you can dig deeper to figure out the root cause.
7. Returns & warranty
Companies use text analytics to analyze data from dealer service professionals across locations regarding warranty claims and returns.
You can gather and extract hidden data in surveys through text analytics algorithms that can sieve verbose, un-contextualized answers to open-ended questions for intelligence.
9. Brand reputation management
Critical text analytics features help the API to derive brand experience data from social media and review forums so that you can influence and manage your brand reputation.
10. Manage online community standards
You can manage your social channels and track online community standards using a text analytics API to ensure there is no cyberbullying happening on your social threads.
A text analysis API can give you the ability to transform big data into business intelligence that you can strategically use, whatever your requirement. Repustate’s text analytics API offers you the most advanced ability to understand data not only as a standalone product but also as a platform with a text analysis dashboard that allows you to see all the insights in the form of charts and graphs.
Once the API is trained to your needs, you can plug it into the current software you are using such as Survey Monkey or Service Now, and extract insights within minutes. You can also set alerts or customize entities yourself, thanks to our no-code technology. With the ability to analyze 23 languages natively, the API contains all the essential text analytics features that a business needs for high performance.