Text mining applications are paving the way for improving business performance and empowering growth. Text mining helps businesses find fruitful information that can be used by sieving through big data gotten from social media, vlogs, customer support tickets, voice of the employee (VoE), voice of the customer (VoC) data, and other such sources.
Business intelligence tools ingest the processed data from text analytics and provide companies with reports for data-driven decisions. These findings pulled from comparisons of historical and current data can help find trends, a real-time purview of how an event has affected a brand or other such vital information.
When done at scale and adopted organization-wide, text mining can lead to synergetic marketing strategies that include client nurturing and brand reputation management. It can also be instrumental in detecting policy violations - whether it’s cyberbullying on social forums, or in returns & warranty claims. Text analytics when combined with sentiment analysis can also lead to vital insights into how different geographies and demographics behave given the same situation.
But first, are text analytics and text analysis the same? These terms get thrown around so often that they are often used interchangeably. In this article, we shed light on this topic as well.
Businesses have a lot to gain by using text mining solutions. They can deliver valuable information and be a catalyst for business intelligence in every industry conceivable. Some common applications of a text mining API in business are as follow.
Text analytics in healthcare can help tremendously in patient management and engagement - right from analysing patient history, to responses, to varied dosages. Text mining is also helpful in Psychiatry where patient notes have been used to predict precedents of certain types of behaviours that call for certain treatments. A study published by the US National Library of Medicine discovered how analyzing texts of EMR reports helped in the prediction of seclusion of patients, and so demonstrating the importance of text analysis in evidence-based clinical decision-making.
Text mining is used to analyze client forums, customer service tickets, call logs, surveys, social media platforms, emails, news feeds, and tweets. It gives businesses better insights into what their clients expect from them, and which areas need focus for improvement. Whether this information is derived from posts and comments, or through videos on channels like Youtube, TikTok, and employee engagement portals, the tool can decipher the data. To analyze the data that is in audio and video formats, it uses Video Content Analysis (VCA) and Audio Analysis.
It is crucial that a company’s public image is impeccable, especially in today’s cancel culture. Text mining allows you to understand data captured from social media listening and voice of customer (VoC) programs by analysing tweets, comments, news articles, and other feedback that mention it or anything or anybody linked to it. This includes company executives, investors, political parties, and organizations the business supports, employees, and partners. Companies can boost the status of their reputation in real-time by taking measures to thwart the crisis.
A text mining helps in ensuring that community standards are followed in the tweets and comments on your social channels. Keywords can be categorized as threats and the text analysis engine will pick them up, including synonyms or misspelled words that are sometimes used to dodge the engine. The same principle is applied to all forms of online communications including emails. This is extremely useful in not only cases of cyberbullying but also in public security measures that are taken during public events to ensure the safety of all present. Video and audio files can also be analyzed simultaneously because the engine transcribes the files from speech to text and processes them for threat and policy violation detection.
Search engines like Bing and Google use text mining to identify spam and filler content in content marketing websites. The engine can identify spelling variations, context, and intent, and so mark an email as spam; or it can penalize a company website that has been trying to increase its search ranking by keyword stuffing or other tactics. Similarly, a company’s own search engine can be optimized and powered with a text analytics API. In doing so, it gives you an organization-wide capability to semantically search all your documents including videos from webinars, training materials, or interviews. For example, Repustate’s search inside video capability allows you to search for words, text overlays, logos, and images across your entire video library by identifying relevant topics, themes, and subject matters.
There is so much that an organization can do when it listens to its customers first-hand. While surveys mean that a customer has to take time out of their day, comments on social media are usually spontaneous and informal. This means that with social media listening, there is so much more that you can learn from information present there about things that a survey wouldn’t have included. Social media has also made it possible for companies to have a direct connection to customers, leading to more personalized interactions, and sometimes even funny online exchanges. Companies like Netflix, McDonald’s, Wendys, and even Microsoft have had hilarious and witty social media exchanges with their customers, while at the same time using this very data to improve existing user interface and customer experience.
A very important aspect in medical treatments and clinical trials, new product development, real estate planning, and other such highly monetized and time-sensitive areas, is finding patterns in data, both historical and current. Text analytics enables companies to study the patterns in data for diverse areas such as consumer behavior, events that can affect oil prices and foreign exchange rates, weather forecasting, and such. Patterns and trends can also be crucial to formulating new policies for security and surveillance as well as traffic regulations to solve congestion problems on high-frequency routes, or in immigration policies.
Text analysis is crucial in understanding data that a company can receive from hundreds of dealer service professionals, spread across locations with issues of warranty claims, and returns. A good text mining tool can read and categorize data even if it contains misspellings, acronyms, shorthand, technical words, or any other inconsistency. Whether it’s large corporations like Costco or Walmart or small & medium businesses (SMBs) in industries as varied as tyres, automotive equipment or electronic white goods, a text analysis software can study both customer and technician comments entered into the warranty claim system, that the manufacturer can then analyze for reference and corrective measures.
Whether it’s through reviews on social media or emails, or market research surveys, a smart text analytics API can recognize and categorize the topics and themes thanks to its machine-learned training. Open-ended questions that have verbose and sometimes cryptic answers in surveys can lead to inaccurate analysis if done manually because of human error and bias. A text analytic solution, on the other hand, uses techniques such as natural language processing (NLP) and aspect-based sentiment analysis to ensure that different aspects and themes present in a single review are all taken into account.
Finding the right candidate to hire can be made easy with text mining. It can go through thousands of records in a recruitment database to find the right candidate using keyword analysis. Ensuring that your star employees are happy at work means you can significantly reduce your employee attrition rate. Using voice of the employee (VoE) feedback programs including voice, chat, and video platforms throughout the employee journey can give you valuable insights on how to ensure a nurturing work environment, and deep employee-employer engagement.
The best in the industry in speed and accuracy, Repustate’s text analysis tool is also powered by sophisticated video AI. It can be customized for sentiment rules no matter what industry your company is in. We have been doing this for more than a decade, which means our corpus for different industries is formidable.
Repustate’s text analysis software also captures text from video formats using optical character recognition, thanks to its video content analysis capability. It is specially built for social media, which means that it not only understands regular language but also short forms and slang, emoticons and emojis, and hashtags. No matter where your customers choose to speak, brands can understand the meaning behind their words.
Repustate is a powerhouse in multi-linguistics, having developed a corpus that covers 23 languages and dialects. It’s time-consuming, hard work that data scientists put in as they manually collate this enormous corpus. And because of this, Repustate’s engine never needs to translate text into English first, as most other text mining tools do, to analyze them for the sentiment. This is truly the secret behind our high accuracy that is still beyond even our competitors in the Gartner Magic Quadrant.
Repustate’s text analytics tool is available as an API, and as an on-premise installation, so you never have to worry about data security or workflow downtime.
