)) Meaning In Text

[Takako Dito]

Contentanalysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts. As an example, researchers can evaluate language used within a news article to search for bias or partiality. Researchers can then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of surrounding the text.

Definition 2: An interpretive and naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity, and generalizability) (from Ethnography, Observational Research, and Narrative Inquiry, 1994-2012).

There are two general types of content analysis: conceptual analysis and relational analysis. Conceptual analysis determines the existence and frequency of concepts in a text. Relational analysis develops the conceptual analysis further by examining the relationships among concepts in a text. Each type of analysis may lead to different results, conclusions, interpretations and meanings.

Typically people think of conceptual analysis when they think of content analysis. In conceptual analysis, a concept is chosen for examination and the analysis involves quantifying and counting its presence. The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. Explicit terms are easy to identify. Coding of implicit terms is more complicated: you need to decide the level of implication and base judgments on subjectivity (an issue for reliability and validity). Therefore, coding of implicit terms involves using a dictionary or contextual translation rules or both.

To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis. Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question.

2. Decide how many concepts to code for: develop a pre-defined or interactive set of categories or concepts. Decide either: A. to allow flexibility to add categories through the coding process, or B. to stick with the pre-defined set of categories.

5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. The researcher can code for exactly what he/she wants to code. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity.

7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize errors far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding.

8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted, or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified.

Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts. Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts.

To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized. Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results.

Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text.

Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Cognitive mapping attempts to create a model of the overall meaning of the text such as a graphic map that represents the relationships between concepts.

1. Determine the type of analysis: Once the sample has been selected, the researcher needs to determine what types of relationships to examine and the level of analysis: word, word sense, phrase, sentence, themes.

2. Reduce the text to categories and code for words or patterns. A researcher can code for existence of meanings or words.

3. Explore the relationship between concepts: once the words are coded, the text can be analyzed for the following:

4. Code the relationships: a difference between conceptual and relational analysis is that the statements or relationships between concepts are coded.

5. Perform statistical analyses: explore differences or look for relationships among the identified variables during coding.

6. Map out representations: such as decision mapping and mental models.

Reliability: Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis:

Closeness of categories: this can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. Using multiple classifiers, a concept category that may be an explicit variable can be broadened to include synonyms or implicit variables.

Generalizability of the results to a theory: dependent on the clear definitions of concept categories, how they are determined and how reliable they are at measuring the idea one is seeking to measure. Generalizability parallels reliability as much of it depends on the three criteria for reliability.

A more powerful tool when combined with other research methods such as interviews, observation, and use of archival records. It is very useful for analyzing historical material, especially for documenting trends over time.

NLP research has enabled the era of generative AI, from the communication skills of large language models (LLMs) to the ability of image generation models to understand requests. NLP is already part of everyday life for many, powering search engines, prompting chatbots for customer service with spoken commands, voice-operated GPS systems and digital assistants on smartphones.

NLP also plays a growing role in enterprise solutions that help streamline and automate business operations, increase employee productivity and simplify mission-critical business processes.

A natural language processing system can work rapidly and efficiently: after NLP models are properly trained, it can take on administrative tasks, freeing staff for more productive work. Benefits can include:

Faster insight discovery: Organizations can find hidden patterns, trends and relationships between different pieces of content. Text data retrieval supports deeper insights and analysis, enabling better-informed decision-making and surfacing new business ideas.

Greater budget savings: With the massive volume of unstructured text data available, NLP can be used to automate the gathering, processing and organization of information with less manual effort.

Quick access to corporate data: An enterprise can build a knowledge base of organizational information to be efficiently accessed with AI search. For sales representatives, NLP can help quickly return relevant information, to improve customer service and help close sales.

Biased training: As with any AI function, biased data used in training will skew the answers. The more diverse the users of an NLP function, the more significant this risk becomes, such as in government services, healthcare and HR interactions. Training datasets scraped from the web, for example, are prone to bias.

Misinterpretation: As in programming, there is a risk of garbage in, garbage out (GIGO). NLP solutions might become confused if spoken input is in an obscure dialect, mumbled, too full of slang, homonyms, incorrect grammar, idioms, fragments, mispronunciations, contractions or recorded with too much background noise.

Tone of voice: When people speak, their verbal delivery or even body language can give an entirely different meaning than the words alone. Exaggeration for effect, stressing words for importance or sarcasm can be confused by NLP, making the semantic analysis more difficult and less reliable.

NLP combines the power of computational linguistics together with machine learning algorithms and deep learning. Computational linguistics is a discipline of linguistics that uses data science to analyze language and speech. It includes two main types of analysis: syntactical analysis and semantical analysis. Syntactical analysis determines the meaning of a word, phrase or sentence by parsing the syntax of the words and applying preprogrammed rules of grammar. Semantical analysis uses the syntactic output to draw meaning from the words and interpret their meaning within the sentence structure.

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