How Does A Text Analytics API Work?

Companies are using machine learning solutions like a text API to gather important business insights from a wide array of data sources, types, and industries. Text analytics allows companies to thus scan social media platforms, review websites, news videos and websites, survey data, healthcare systems, customer relationship management tools, content management tools, multilingual data, and numerous other sources to extract meaningful information for a variety of reasons.

This article sheds light on how an artificial intelligence (AI)-driven text API works behind the scenes in order to provide users with critical insights from seemingly scattered and unstructured data.

What Is Text Analytics?

Text analytics is the process of using machine learning (ML) to distill relevant, meaningful information from unstructured text data such as for customer feedback analysis. Text analytics allows the user to identify patterns in data and discover sentiment expressed in text in order to gain actionable insights. The text analytics process uses natural language processing (NLP) tasks to understand the text in the language that it is in, and then converts it into machine language so that the text mining API software can analyze it to extract relevant insights.

How Does A Text Mining API Work?

A text mining API or application programming interface is the bridge between two software programs so that they can interact with each other, transfer information, and analyze data. The text analytics API helps a data analysis platform to connect to an application or website, gather the data, convert it into binary code that can be understood by the computer, and helps machine learning algorithms analyze the data to decipher granular information. These results can then be presented on any visualization screen in the form of reports.

To better understand how a text mining API works, let us consider the three main steps under which the entire text analysis processing happens.

Step 1: Text collection

The first step is the actual gathering of data for the purposes of data analysis. This can be done in two ways.

• Live APIs - The text API can connect with the Live API of the data source. This is usually the case when we aim for social listening on Instagram or live Twitter data analysis. The same is the case when the text mining API is used for news sentiment analysis and gathers data from news websites.
• Manual - The text API can also work with data that is manually loaded in the form of a .csv file. This is useful in instances where, for example, multiple surveys are used to analyze customer experience.

Step 2: Data processing

For data processing, the text mining API uses text-to-speech software and natural language processing to convert data of all types into text so that it can be analyzed for sentiment and finding significant patterns. This happens in the following steps.

• Audio transcription - Any audio files found in podcasts or video data is transcribed.
• Caption overlay - All captions in the video data are extracted and captured for the text pipeline. They are later analyzed for topics, entities, and aspects.
• Emojis and hashtags - All emoticons and hashtags are extracted from the text and added to the pipeline as well.
• Text extraction- All the text is then cleaned, prepared, and extracted for data analysis. So now, ultimately, all the data from every format is collected and pre-processed for analysis.

Step 3: Text analysis

Now that the text data is ready for analysis, the text API processes it all using multiple ML tasks such as semantic clustering, multilingual data processing, tagging, topic classification, and more. Let’s see them in detail.

• Natural Language Processing (NLP) - NLP tasks ensure that all the data is linguistically analyzed for grammar, tonality, sentence structure, recurring nouns, adjectives, aspects, etc. so that the text API is able to process all of it for sentiment at a later stage.
• Semantic Clustering - The text API creates groups of clusters to which it assigns semantically similar data. This is an important function because it increases the accuracy of the insights as it removes redundancies and the possibility of double negatives or positives.
• Named Entity Recognition (NER) - Named entity recognition tasks recognize any “named” entity such as location, currency, person, object, etc so that they can be assigned sentiment later in the process.
• Language Detection & Analysis - Native part-of-speech taggers recognize the language the data is in so that they can linguistically analyze the text in its native tongue. This makes sure that the data is analyzed without machine translations for precision.
• Custom Tags - Custom tags are created for aspects and themes so that once the data is processed, the text API will automatically segregate the data based on these custom tags.
• Topic Classification - Topic classification recognizes a theme in the data and extracts it for sentiment analysis.
• Sentiment Analysis - Every topic, theme, and aspect is now analyzed for the sentiment. They are given a score between -1 and +1. Finally, the entire project/brand is assigned a sentiment, which is the overall sentiment score. Thus, the text mining API can provide you with aspect-based sentiment scores for granular marketing insights as well as a holistic score of brand sentiment.

Eventually, in order to see the insights in the form of reports based on the data analysis conducted by the text mining API, you could integrate it into a visualization tool of your choice.

Why Do Companies Use A Text API?

A text API is a convenient way to analyze data to gather crucial business and marketing insights. This is especially so when you don’t have a proprietary text API of your own or don’t want to purchase a complete solution with a comprehensive dashboard if you already have data visualization software such as PowerBI.

Text analysis helps companies to dig deeper into product reviews, social media comments analysis for brand experience insights, voice-of-patient feedback data, traditional and non-conventional news source analysis for current and emerging trends, emails and chatbot histories for customer experience analysis, discovering TikTok influencers, and much more.

Below are some of the most critical ways a text API benefits companies.

1. Cost-effectiveness

Using a text mining API that is suited to your industry and ready to be integrated into your system for data analysis is much more cost-effective and hassle-free than building your own API from scratch. Even if you were using manual analysis of data, that would include a huge number of man-hours, which in the end is not conducive to data analysis from vast and scattered resources.

2. Unbiased insights

In addition to manual data analysis being time-consuming, it also can be peppered with human biases. This is especially true in the case of marketing surveys, where a negative comment or review is not taken seriously or not given importance based on the data analyst’s perceived biases. Similarly, using a text API for employee experience insights can give you unbiased insights compared to conclusions reached by someone on the human resources team. This is often the case with regard to an employee’s exit interview remarks on elements like work culture, compensation, and other factors, that can get ignored simply because they are from a former employee.

3. Automated, accurate insights

A text API gives automated insights that can be in real-time or periodic based on the data source. Automated insights also mean that there is no need for human intervention and you can set up specific instructions to make sure that the API analyzes only certain aspects, keywords, or topics and be certain of precision in the insights that you get based on your set parameters.

4. Speed & Scalability

Companies depend on a text mining API for scalable data analysis, and one that you can get without wasting hours and hours of precious time. A really great text API can scan and analyze thousands of data points in minutes and offer you speed and agility that are critical in certain industries such as banking or healthcare.

5. Real-time analysis

A text API can give you real-time data analysis for your branding and marketing campaigns, especially from social media listening. This can be particularly useful for TikTok social listening or when you want real-time brand insights from review sentiment analysis during events.

6. Speed to market

A text analytics API can help you reach market needs as soon as possible and also prepare you for emerging industry trends. You are in step with industry benchmarks and ahead of your competing brands in a highly dynamic market.

7. Flexibility

A text mining API gives you the flexibility to use a Cloud-based solution for gathering business intelligence, and if you have the need, opt for an on-prem solution behind your firewall for data security.

Conclusion

A text API can solve your need for high-precision insights from any kind of data - whether it’s for customer insights, brand experience insights, or employee experience and engagement. Machine learning algorithms make sure that all important aspects of your data are clustered together and classified for sentiment analysis so that you get the most out of your investment in terms of accuracy, granularity, speed, and scalability.

Repustate’s text analytics API gives you all these benefits in 23 languages. You can choose from a range of data sources to analyze text from including TikTok, Reddit, Amazon reviews, Twitter, Yelp, and others. What’s more, the API integrates seamlessly with visualization tools such as Tableau and Power BI, as well as feedback tools such as SurveyMonkey. Get the high-precision business insights you need for your marketing, brand, and human resources functions in a few clicks.