A worked example of Braun and Clarkes approach to reflexive thematic analysis Quality & Quantity

Similarly, the class scope must be terminated before the global scope ends. More exactly, a method’s scope cannot be started before the previous method scope ends (this depends on the language though; for example, Python accepts functions inside functions). This new scope will have to be terminated before the outer scope (the one that contains the new scope) is closed. For example, a class in Java defines a new scope that is inside the scope of the file (let’s call it global scope, for simplicity). On the other hand, any method inside that class defines a new scope, that is inside the class scope. A scope is a subsection of the source code that has some local information.

Today, machine learning algorithms and NLP (natural language processing) technologies are the motors of semantic analysis tools. They allow computers to analyse, understand and treat different sentences. As we enter the era of ‘data explosion,’ it is vital for organizations to optimize this excess yet valuable data and derive valuable insights to drive their business goals. Semantic analysis allows organizations to interpret the meaning of the text and extract critical information from unstructured data.

The code C5 offers an exemplar of the provision of sufficient detail to explain what I interpreted from the related data item. A poor example of this code would be to say “the wellbeing guidelines are not relatable” or “not relatable for students”. Understanding codes written in this way would be contingent upon knowledge of the underlying data extract. It is unclear if the positivity mentioned relates to the particular participant, their colleagues, or their students. It can also be seen in this short example that the same code has been produced for both C4 and C9. This code was prevalent throughout the entire dataset and would subsequently be informative in the development of a theme.

What is semantic analysis?

It turns out most programming languages are both interpreted and compiled. A Java source code is first compiled, but not into machine code, rather into a special code called bytecode, which is then interpreted by a special interpreter program, famously known as Java Virtual Machine. The names of themes are also subject to a final review (if necessary) at this point. Naming themes may seem trivial and might subsequently receive less attention than it actually requires.

By sticking to just three topics we’ve been denying ourselves the chance to get a more detailed and precise look at our data. As you can see the semantics is used to make the interactions between the search engine and its users easier, but it also helps the search engine to better understand (and use) the information on any page. Finally, the recent project called inLinks helps you add structured data to your pages based on their own semantic analysis.

Speaking about business analytics, organizations employ various methodologies to accomplish this objective. In that regard, sentiment analysis and semantic analysis are effective tools. By applying these tools, an organization can get a read on the emotions, passions, and the sentiments of their customers.

Semantic-enhanced machine learning tools are vital natural language processing components that boost decision-making and improve the overall customer experience. Today, semantic analysis methods are extensively used by language translators. Earlier, tools such as Google translate were suitable for word-to-word translations. However, with the advancement of natural language processing and deep learning, translator tools can determine a user’s intent and the meaning of input words, sentences, and context. All these parameters play a crucial role in accurate language translation. IBM’s Watson provides a conversation service that uses semantic analysis (natural language understanding) and deep learning to derive meaning from unstructured data.

An experiential orientation to understanding data typically prioritises the examination of how a given phenomenon may be experienced by the participant. This involves investigating the meaning ascribed to the phenomenon by the respondent, as well as the meaningfulness of the phenomenon to the respondent. However, although these thoughts, feelings and experiences are subjectively and inter-subjectively (re)produced, the researcher would cede to the meaning and meaningfulness ascribed by the participant (Braun and Clarke 2014).

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Read on to find out more about this semantic analysis and its applications for customer service. NeuraSense Inc, a leading content streaming platform in 2023, has integrated advanced semantic analysis algorithms to provide highly personalized content recommendations to its users. By analyzing user reviews, feedback, and comments, the platform understands individual user sentiments and preferences. Instead of merely recommending popular shows or relying on genre tags, NeuraSense’s system analyzes the deep-seated emotions, themes, and character developments that resonate with users.

As such, any item of information could be double-coded in accordance with the semantic meaning communicated by the respondent, and the latent meaning interpreted by the researcher (Patton 1990). A predominantly inductive approach was adopted in this example, meaning data was open-coded and respondent/data-based meanings were emphasised. The data used in the following example is taken from the qualitative phase of a mixed methods study I conducted, which examined mental health in an educational context. I also wanted to identify any potential barriers to wellbeing promotion and to solicit educators’ opinions as to what might constitute apposite remedial measures in this regard.

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The matrices 𝐴𝑖 are said to be separable because they can be decomposed into the outer product of two vectors, weighted by the singular value 𝝈i. Calculating the outer product of two vectors with shapes (m,) and (n,) would give us a matrix with a shape (m,n). In other words, every possible product of any two numbers in the two vectors is computed and placed in the new matrix. The singular value not only weights the sum but orders it, since the values are arranged in descending order, so that the first singular value is always the highest one. The idea behind using code to express meaning (not just presentation) goes years back, long before Schema.org project was launched.

It is quite typical at this phase that codes, as well as themes, may be revised or removed to facilitate the most meaningful interpretation of the data. As such, it may be necessary to reiterate some of the activities undertaken during phases two and three of the analysis. It may be necessary to recode some data items, collapse some codes into one, remove some codes, or promote some codes as sub-themes or themes.

Just for the purpose of visualisation and EDA of our decomposed data, let’s fit our LSA object (which in Sklearn is the TruncatedSVD class) to our train data and specifying only 20 components. We can arrive at the same understanding of PCA if we imagine that our matrix M can be broken down into a weighted sum of separable matrices, as shown below. What matters in understanding the math is not the algebraic algorithm by which each number in U, V and 𝚺 is determined, but the mathematical properties of these products and how they relate to each other. We want to explain the purpose and the structure of our content to a search engine. That’s how HTML tags add to the meaning of a document, and why we refer to them as semantic tags.

For example, ‘Raspberry Pi’ can refer to a fruit, a single-board computer, or even a company (UK-based foundation). You can foun additiona information about ai customer service and artificial intelligence and NLP. Hence, it is critical to identify which meaning suits the word depending on its usage. Extensive business analytics enables an organization to gain precise insights into their customers. Consequently, they can offer the most relevant solutions to the needs of the target customers.

In other words, we can say that lexical semantics is the relationship between lexical items, meaning of sentences and syntax of sentence. MedIntel, a global health tech company, launched a patient feedback system in 2023 that uses a semantic analysis process to improve patient care. Rather than using traditional feedback forms with rating scales, patients narrate their experience in natural language. MedIntel’s system employs semantic analysis to extract critical aspects of patient feedback, such as concerns about medication side effects, appreciation for specific caregiving techniques, or issues with hospital facilities.

As such, it is important to appreciate the six-phase process as a set of guidelines, rather than rules, that should be applied in a flexible manner to fit the data and the research question(s) (Braun and Clarke 2013, 2020). The first part of semantic analysis, studying the meaning of individual words is called lexical semantics. It includes words, sub-words, affixes (sub-units), compound words and phrases also.

Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. The semantic analysis does throw better results, but it also requires substantially more training and computation. The meaning representation can be used to reason for verifying what is correct in the world as well as to extract the knowledge with the help of semantic representation.

During the level one review, inspection of the prospective sub-theme “sources of negative affect” in relation to the theme “recognising educator wellbeing” resulted in a new interpretation of the constituent coded data items. Participants communicated numerous pre-existing work-related factors that they felt had a negative impact upon their wellbeing. However, it was also evident that participants felt the introduction of the new wellbeing curriculum and the newly mandated task of formally attending to student wellbeing had compounded these pre-existing issues. This resulted in the “sources of negative affect” sub-theme being split into two new sub-themes; “work-related negative affect” and “the influence of wellbeing promotion”.

Semantic analysis, often referred to as meaning analysis, is a process used in linguistics, computer science, and data analytics to derive and understand the meaning of a given text or set of texts. In computer science, it’s extensively used in compiler design, where it ensures that the code written follows the correct syntax and semantics of the programming language. In the context of natural language processing and big data analytics, it delves into understanding the contextual meaning of individual words used, sentences, and even entire documents. By breaking down the linguistic constructs and relationships, semantic analysis helps machines to grasp the underlying significance, themes, and emotions carried by the text. At this point in the analysis, I assembled codes into initial candidate themes. The theme “best practice in wellbeing promotion” was clearly definable, with constituent coded data presenting two concurrent narratives.

Level one is a review of the relationships among the data items and codes that inform each theme and sub-theme. If the items/codes form a coherent pattern, it can be assumed that the candidate theme/sub-theme makes a logical argument and may contribute to the overall narrative of the data. At level two, the candidate themes are reviewed in relation to the data set.

If we have only two variables to start with then the feature space (the data that we’re looking at) can be plotted anywhere in this space that is described by these two basis vectors. Now moving to the right in our diagram, the matrix M is applied to this vector space and this transforms it into the new, transformed space in our top right corner. In the diagram below the geometric effect of M would be referred to as “shearing” the vector space; the two vectors 𝝈1 and 𝝈2 are actually our singular values plotted in this space. The extra dimension that wasn’t available to us in our original matrix, the r dimension, is the amount of latent concepts. Generally we’re trying to represent our matrix as other matrices that have one of their axes being this set of components. You will also note that, based on dimensions, the multiplication of the 3 matrices (when V is transposed) will lead us back to the shape of our original matrix, the r dimension effectively disappearing.

Organizations keep fighting each other to retain the relevance of their brand. There is no other option than to secure a comprehensive engagement with your customers. Businesses can win their target customers’ hearts only if they can match their expectations with the most relevant solutions. So far we have seen in detail static and dynamic typing, as well as self-type. These are just two examples, among many, of what extensions have been made over the years to static typing check systems.

This kind of system can detect priority axes of improvement to put in place, based on post-purchase feedback. The company can therefore analyze the satisfaction and dissatisfaction of different consumers through the semantic analysis of its reviews. Using Syntactic analysis, a computer would be semantic analysis example able to understand the parts of speech of the different words in the sentence. Based on the understanding, it can then try and estimate the meaning of the sentence. In the case of the above example (however ridiculous it might be in real life), there is no conflict about the interpretation.

In fact, there’s no exact definition of it, but in most cases a script is a software program written to be executed in a special run-time environment. There may be need for more information, and these will depend on the language specification. Therefore, the best thing to do is to define a new class, or some type of container, and use that to save information for a scope.

With the help of meaning representation, we can represent unambiguously, canonical forms at the lexical level. In this component, we combined the individual words to provide meaning in sentences. Lexical analysis is based on smaller tokens but on the contrary, the semantic analysis focuses on larger chunks. Therefore, the goal of semantic analysis is to draw exact meaning or dictionary meaning from the text. The automated process of identifying in which sense is a word used according to its context.

When transcription of all interviews was complete, I read each transcripts numerous times. At this point, I took note of casual observations of initial trends in the data and potentially interesting passages in the transcripts. I also documented my thoughts and feelings regarding both the data and the analytical process (in terms of transparency, it would be beneficial to adhere to this practice throughout the entire analysis). Some preliminary notes made during the early iterations of familiarisation with the data can be seen in Box 1. It will be seen later that some of these notes would go on to inform the interpretation of the finalised thematic framework.

• Ultimately, the provided example of how to implement the six-phase analysis is easily transferable to many contexts and research topics.
• In the dataset we’ll use later we know there are 20 news categories and we can perform classification on them, but that’s only for illustrative purposes.
• When Schema.org was created in 2011, website owners were offered even more ways to convey the meaning of a document (and its different parts) to a machine.
• Read on to find out more about this semantic analysis and its applications for customer service.
• I also documented my thoughts and feelings regarding both the data and the analytical process (in terms of transparency, it would be beneficial to adhere to this practice throughout the entire analysis).

As we conclude this tutorial, let’s recap the significant strides we’ve made in understanding and applying advanced NLP techniques using Sentence Transformers and MLflow. Demonstrate the use of the SimilarityModel to compute semantic similarity between sentences after logging it with MLflow. We create a new MLflow Experiment so that the run we’re going to log our model to does not log to the default experiment and instead has its own contextually relevant entry. The SimilarityModel is a tailored Python class that leverages MLflow’s flexible PythonModel interface.

Theme names are the first indication to the reader of what has been captured from the data. The overriding tendency may be to create names that are descriptors of the theme. Braun and Clarke (2013, 2014, 2020) encourage creativity and advocate the use of catchy names that may more immediately capture the attention of the reader, while also communicating an important aspect of the theme. To this end, they suggest that it may be useful to examine data items for a short extract that could be used to punctuate the theme name. The qualitative phase of this study, from which the data for this example is garnered, involved eleven semi-structured interviews, which lasted approximately 25–30 min each. Participants consisted of core-curriculum teachers, wellbeing curriculum teachers, pastoral care team-members and senior management members.

Once that happens, a business can retain its

customers in the best manner, eventually winning an edge over its competitors. Understanding

that these in-demand methodologies will only grow in demand in the future, you

should embrace these practices sooner to get ahead of the curve. Well, suppose that actually, “reform” wasn’t really a salient topic across our articles, and the majority of the articles fit in far more comfortably in the “foreign policy” and “elections”. Thus “reform” would get a really low number in this set, lower than the other two. An alternative is that maybe all three numbers are actually quite low and we actually should have had four or more topics — we find out later that a lot of our articles were actually concerned with economics!

This phase can be quite time consuming and requires a degree of patience. No attempt was made to prioritise semantic coding over latent coding or vice-versa. Rather, semantic codes were produced when meaningful semantic information was interpreted, and latent codes were produced when meaningful latent information was interpreted.

If the number is zero then that word simply doesn’t appear in that document. This article assumes some understanding of basic NLP preprocessing and of word vectorisation (specifically tf-idf vectorisation). Latent Semantic Analysis (LSA) is a popular, dimensionality-reduction techniques that follows the same method as Singular Value Decomposition. LSA ultimately reformulates text data in terms of r latent (i.e. hidden) features, where r is less than m, the number of terms in the data. I’ll explain the conceptual and mathematical intuition and run a basic implementation in Scikit-Learn using the 20 newsgroups dataset.

If we’re looking at foreign policy, we might see terms like “Middle East”, “EU”, “embassies”. For elections it might be “ballot”, “candidates”, “party”; and for reform we might see “bill”, “amendment” or “corruption”. So, if we plotted these topics and these terms in a different table, where the rows are the terms, we would see scores plotted for each term according to which topic it most strongly belonged.

However, codebook approaches are more akin to the reflexive approach in terms of the prioritisation of a qualitative philosophy with regard to coding. Proponents of codebook approaches would typically forgo positivistic conceptions of coding reliability, instead recognising the interpretive nature of data coding (Braun et al. 2019). “Semantics” refers to the concepts or ideas conveyed by words, and semantic analysis is making any topic (or search query) easy for a machine to understand. “Semantics” refers to the concepts or ideas conveyed by words, and semantic analysis is making any topic (or search query) easy for a machine to understand.

For example, if a user expressed admiration for strong character development in a mystery series, the system might recommend another series with intricate character arcs, even if it’s from a different genre. In semantic analysis with machine learning, computers use word sense disambiguation to determine which meaning is correct in the given context. It’s not just about understanding text; it’s about inferring intent, unraveling emotions, and enabling machines to interpret human communication with remarkable accuracy and depth. From optimizing data-driven strategies to refining automated processes, semantic analysis serves as the backbone, transforming how machines comprehend language and enhancing human-technology interactions.