In the present day, every company has access to a large volume of unstructured data, which can help in strategic business decisions. One of the essential data types is textual data. For example, a business must know what customers feel about their products or services. Similarly, in research, it is essential to understand what a respondent is saying on his own. When we prepare a structured questionnaire to collect responses, we ask close-ended questions that give us direct answers, and we have open-end or free answers questions in which the respondent writes sentences. These two types of questions provide two kinds of data one in a structured format and another in the unstructured format as a text format. The whole ecosystem of the data is of the two forms only. In an Unstructured format, there is further addition of Audio, video, and images.
Text Analytics is one area by which the text or responses collected through different media are analyzed using tools and algorithms. Previously, the text data was investigated by the manual reading of each sentence. With the advancement and availability of the latest tools, the text analytics process is moving towards automation. Several methods and tools available can quantify the text data to provide patterns, trends, and insights. Natural Language Processing (NLP) is one of the widely used concepts that help in analyzing text.
The input for text analytics or text mining comes from online reviews, twitter feeds, Facebook posts, emails, survey questions, and customer feedback. Due to the boost of social media platforms, everyone has the power to write content related to anything. These written contents are a gold mine for respective stakeholders. Once the input is in place, the software or tools perform further analysis. To provide the full context, let us consider the process followed in the R software.
Firstly, in the R environment, a corpus is built using the input data that is simply a collection of unstructured text. Once the corpus is ready, then the data cleaning is done by removing the numbers, punctuation, stop words, whitespace, etc. After that, the tool generates a Document Term Matrix. The document term matrix is a matrix in which the words are present as columns with their count as rows. This matrix is used for further analysis to get valuable insights from the text data.
The text analytics results come in the form of word count, frequencies, word clouds, word association, correlations, and clusters. Apart from these analyses, the tool also provides the facility to conduct sentiment analysis of text data. The sentimental analysis is the process by which one can interpret and classify the data into three emotions: positive, negative, and neutral. With the help of the content analysis, a business or research scholar can identify respondent sentiment towards the company, brands, products, or research scenarios. If a business can know what a customer feels, then they can improve their offerings.
Artificial Intelligence is helping the text analytics domain in a big way. The infrastructure for holding a massive amount of data is available these days. So many cloud service providers give services for storing and mining unstructured data. The future is looking promising for text analytics.
(The above article was published as an Abstract at ICMIT2020 conference)