Draft:Semantic Brand Score

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Semantic Brand Score

The Semantic Brand Score (SBS) offers a method for evaluating the importance of one or multiple brands across various contexts, leveraging textual data, including big data. Rooted in graph theory, this metric integrates text mining and social network analysis techniques. Inspired by Keller's conceptualization of brand equity, the Semantic Brand Score introduces a novel approach to measuring semantic brand importance.

The SBS is a composite indicator with three dimensions: prevalence, diversity and connectitivy.

Traditional assessments of brand equity typically rely on models involving consumer surveys or financial assessments. In contrast, the Semantic Brand Score derives from the analysis of spontaneous expressions found in texts, thereby circumventing potential cognitive biases associated with direct interviews. This metric can be computed by examining sources such as newspaper articles, online forums, scientific papers, or social media posts, reflecting diverse stakeholder perspectives.

Definition and calculation

Pre-processing

To compute the Semantic Brand Score, it is necessary to first convert the analyzed texts into word networks, essentially graphs where each node signifies a word. Connections between words are established based on their co-occurrence within a specified proximity, such as within a sentence. Pre-processing of natural language is recommended to refine texts, which may involve tasks like eliminating stopwords and word affixes through stemming. For instance, here's a sample network derived from pre-processing the sentence "The dawn is the appearance of light - usually golden, pink or purple - before sunrise".

Prevalence

This dimension measures the frequency of brand name usage, indicating how often a brand is explicitly referenced in a corpus. The prevalence factor is closely associated with brand awareness, suggesting that a brand mentioned frequently in a text is more familiar to its authors. Likewise, frequent mentions of a brand name enhance its recognition and recall among readers.

Diversity

This dimension assesses the variety of words linked with a brand, focusing on textual associations. These textual associations refer to the words used alongside a particular brand. Measurement involves employing the degree centrality indicator, reflecting the number of connections a brand node has in the semantic network. Alternatively, an approach using distinctiveness centrality has been proposed, assigning greater significance to unique brand associations and reducing redundancy. The rationale is that distinctive textual associations enriches discussions about a brand, thereby enhancing its memorability.

Diversity can be calculated for the brand node in a semantic network, i.e. a weighted undirected graph G, made of n nodes and m arcs. If two nodes, i and j, are not connected, then $w_{ij}=0$, otherwise the weight of the arc connecting them is $w_{ij}\geq 1$. In the following, $g_{j}$ is the degree of node j and $I_{(f)}$ is the indicator function which equals 1 if $f=TRUE$, i.e. if there is an arc connecting nodes i and j.

$DI(i)=\sum _{j=1,j\neq i}^{n}\log _{10}{\frac {n-1}{g_{j}}}I_{(w_{ij}>0)}$.

Connectivity

This third dimension evaluates a brand's connectivity within broader discourse, indicating its capacity to serve as a bridge between various words/concepts (nodes) in the network. Essentially, it gauges the brand's brokerage power, its ability to connect different words, groups of words, or topics together. The calculation hinges on the weighted betweenness centrality metric.

Semantic Brand Score

The Semantic Brand Score is a composite measure derived from the sum of the standardized values of prevalence, diversity, and connectivity. SBS standardization is typically performed by subtracting the mean from the raw scores of each dimension and then dividing by the standard deviation . This process takes into account the scores of all relevant words in the corpus.

Measuring SBS encapsulates the full concept of brand importance (which cannot be captured by considering one dimension alone). To illustrate, consider a scenario where a brand is frequently referenced but in a repetitive manner, with numerous posts echoing the same sentiment, such as "PuebloWine is the best drink of all time." Here, prevalence would be high, yet diversity would be low. Conversely, a brand frequently cited across diverse contexts would exhibit both high prevalence and diversity. However, connectivity might remain low if the brand is only discussed within a niche of broader discourse. When a brand bridges various topics, its connectivity rises. For instance, the "PuebloWine" brand could be central in discussions on red wine but peripheral in conversations about bar cocktails.

See also

• Big data
• Brand equity
• Brand management
• Brand valuation
• Graph theory
• Natural language processing
• Network theory
• Semantic analytics
• Social network analysis
• Text mining

External links

• https://towardsdatascience.com/calculating-the-semantic-brand-score-with-python-3f94fb8372a6. Tutorial for the calculation of the Semantic Brand Score using Python

This page will be placed in the following categories if it is moved to the article namespace.

Categories:

• Graph algorithms
• Graph theory
• Network analysis
• Text mining
• Brand management
• Network theory
• Brand valuation