15Oct - by aiuniverse - 0 - In Data Mining

Source: analyticsinsight.net

Stipulating text mining applications to drive data-driven decisions.

A majority of organizations today glean and store voluminous amounts of data and this data is relentlessly growing at an exponential rate. Processing this amount of data, however, becomes a challenge for businesses with conventional tools. In this way, text mining has garnered much attention in the last few years, translating unstructured text data into meaningful and actionable insights. Also known as text analytics, text mining leverages various AI technologies to automatically process data and produce valuable information, enabling enterprises to make data-driven decisions.

In general, text mining refers to a multidisciplinary field incorporating the likes of data mining, statistics, machine learning, information retrieval, and computational linguistics.

Here is a look at the best real-world text mining applications demonstrating the pragmatic data techniques and impacting businesses.

Data Extraction

Data extraction technique refers to the extraction of insightful information from enormous trove of textual data. It involves the retrieval of data from diverse sources. The information extracted is then stored for further analysis or use in a separate database. Afterward, precision and reminiscence processes are used to check the efficiency and relevancy of outcomes to drive data-driven decisions.

Knowledge Management

Knowledge management ensures that the right information is delivered to the right person, at the right time. It is intended to serve business practices for coalescing the vast amounts of information generated from meetings, presentations, analytic papers, training materials, and others. Since organizations are challenged with a large volume of information, knowledge management software based on text mining offers a fair and reliable solution. Various text mining and NLP techniques can be used as an effective knowledge management system assisting in the extraction of germane insights from unstructured textual data.

Cybercrime Prevention

With a surging number of cybercrimes such as phishing, data breach and cyberbullying, among others, integrating a cybersecurity app with text mining capabilities can be assistive for companies to spot malicious activities. This can help lower financial cybercrime instances, such as phishing and ransomware attacks. Text mining solutions can also detect words that are commonly used for bullying, threatening, or other harmful activities on the internet.

Customer Care Service

Customer care representatives always deal with tons of requests and queries, which can become complex to handle in real-time. This over-flood of data can lead to degradation in the quality of customer care services, eventually tarnishing a brand reputation and driving away customers. Text mining solutions, along with NLP software are frequently being adopted by customer service providers to improve customer experience using distinct bid data sources, such as surveys, trouble tickets and customer call notes, and others. This is significantly vital to improve the quality, effectiveness and speed in resolving customer queries.

Contextual Advertising

Advertising has been shifted from the traditional way of ads placement to a digital one. The failure or success of ads placement majorly relies on what ads are running and where they are displaying. To this context, enterprises have started prioritizing contextual advertising, the practice of placing ads on web pages based on the content of those pages. It is a great ad targeting technique as it allows advertisers to ensure their banner ads are being seen by a pertinent audience. By using text mining, businesses can run contextual web ad campaigns that give them a high ROI. Text mining assists them to understand the context on a webpage and place ads on that.

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