The term that best describes the application of technological tools to analyze textual data is sentiment analysis. This technique specifically focuses on extracting and identifying subjective information, such as emotions and opinions, from text. It involves using software and algorithms to understand the sentiment behind the words, determining whether they convey positive, negative, or neutral sentiments.
Sentiment analysis is increasingly used in various fields, including marketing, where it helps companies gauge customer opinions about products, services, or brand perception across social media platforms, reviews, and other textual sources. By employing advanced technologies like natural language processing (NLP) and machine learning, companies can analyze large volumes of text quickly, uncovering insights that manual analysis might miss.
The other terms, while related to data analysis, do not specifically target the extraction of sentiment or emotions from textual content in the same way. Data mining refers to the broader process of discovering patterns in large datasets, which can include various data types beyond just text. Content analysis is more focused on the systematic examination of communication materials, often categorizing what is present rather than interpreting the sentiment behind it. Qualitative research encompasses a wide range of non-numerical data analysis methods that seek to understand underlying reasons and motivations, but it is not specifically centered on textual sentiment