Generally, humans are far more perceptive at teasing out these contextual factors than machines (although, AI is giving us a run for our money). When identifying words and phrases, you’re also going to want to look at the surrounding context and possibly cultural interpretations of what is going on (Mayring, 2015). In many ways, textual analysis lends itself best to qualitative analysis. This can include techniques like sentiment analysis, topic modeling, named entity recognition, and more. You could analyze several of the above features, namely, content, form, and structure, from a quantitative perspective using computational linguistics and natural language processing (NLP) analysis.įrom this approach, you would use algorithms to extract useful information or insights about frequency of word and phrase usage, etc. The above features may be examined through quantitative or qualitative research designs, or a mixed-methods angle. Form and structure: The layout, sequence, and organization of the text and how these elements contribute to its meanings (Metoyer et al., 2018).Authorship: Who created the text, their background and perspectives, and how these might influence the text.Audience: Who the text is intended for, how it’s received, and the effect it has on its audience.Context: When and where the text was created, the culture and society it reflects, and the circumstances surrounding its creation and distribution. Content: What is being said or conveyed in the text, including explicit and implicit meanings, themes, or ideas. When conducting a textual analysis, you’ll need to consider a range of factors within the text that are worthy of close examination to infer meaning. For a dive into the analysis of multimodal texts, I recommend my article on content analysis, where I explore the study of texts like television advertisements and movies in detail. McKee uses the term ‘textual analysis’ to also refer to text types that are not just written, but multimodal.
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