When you put machines to work on organizing and analyzing your text data, the insights and benefits are huge. However, it's likely that the manager also wants to know which proportion of tickets resulted in a positive or negative outcome?īy analyzing the text within each ticket, and subsequent exchanges, customer support managers can see how each agent handled tickets, and whether customers were happy with the outcome.īasically, the challenge in text analysis is decoding the ambiguity of human language, while in text analytics it's detecting patterns and trends from the numerical results. In this instance, they'd use text analytics to create a graph that visualizes individual ticket resolution rates. Let's say a customer support manager wants to know how many support tickets were solved by individual team members.
If a machine performs text analysis, it identifies important information within the text itself, but if it performs text analytics, it reveals patterns across thousands of texts, resulting in graphs, reports, tables etc. Text analysis delivers qualitative results and text analytics delivers quantitative results. To avoid any confusion here, let's stick to text analysis. The terms are often used interchangeably to explain the same process of obtaining data through statistical pattern learning. Text Analyticsįirstly, let's dispel the myth that text mining and text analysis are two different processes. You can us text analysis to extract specific information, like keywords, names, or company information from thousands of emails, or categorize survey responses by sentiment and topic.
Companies use text analysis tools to quickly digest online data and documents, and transform them into actionable insights. Text analysis (TA) is a machine learning technique used to automatically extract valuable insights from unstructured text data.
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