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Draft:Semantic Brand Score

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Semantic Brand Score

The Semantic Brand Score (SBS) is a measure of brand importance that can be calculated on textual data, including big data, in different contexts[1][2][3]. The measure is rooted in graph theory and partly connected to Keller's[4] conceptualization of brand equity[5].

The SBS is a composite indicator with three dimensions: prevalence, diversity and connectitivy[6][7].

The metric can be computed by examining different text sources, such as newspaper articles, online forums, scientific papers, or social media posts[8][9][10].

Definition and calculation

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Pre-processing

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To compute the Semantic Brand Score, it is necessary to convert the analyzed texts into word networks, i.e., 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 preliminary used to refine texts, involving tasks like eliminating stopwords and word affixes through stemming[11]. Here is a sample network derived from pre-processing the sentence "The dawn is the appearance of light - usually golden, pink or purple - before sunrise".

 

Prevalence

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This dimension measures the frequency of brand name usage, indicating how often a brand is explicitly referenced in a corpus. The prevalence factor is associated with brand awareness, suggesting that a brand mentioned frequently in a text is more familiar to its authors[6][7][10]. Likewise, frequent mentions of a brand name enhance its recognition and recall among readers.

Diversity

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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[1]. Alternatively, an approach using distinctiveness centrality[12] has been proposed, assigning greater significance to unique brand associations and reducing redundancy. The rationale is that distinctive textual associations enrich 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  , otherwise the weight of the arc connecting them is  . In the following,   is the degree of node j and   is the indicator function which equals 1 if  , i.e. if there is an arc connecting nodes i and j.

 .

Connectivity

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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[1][2][3]. It captures a 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[3][13].

Semantic Brand Score

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The Semantic Brand Score indicator is given by the sum of the standardized values of prevalence, diversity, and connectivity[1][6][7]. SBS standardization is typically performed by subtracting the mean from the raw scores of each dimension and then dividing by the standard deviation [3]. This process takes into account the scores of all relevant words in the corpus.

SBS measures brand importance, a construct that cannot be understood by examining a single dimension alone. Indeed, a brand name might be frequently mentioned in posts repeating the same content, indicating high prevalence but low diversity. Conversely, a brand cited across diverse contexts would show both high prevalence and diversity. Connectivity, which increases when a brand bridges various topics, could still remain low if the brand is discussed only within a niche of the overall discourse.

See also

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References

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  1. ^ a b c d Schlaile, Michael P.; Bogner, Kristina; Muelder, Laura (2021). "It's more than complicated! Using organizational memetics to capture the complexity of organizational culture". Journal of Business Research. 129: 801–812. doi:10.1016/j.jbusres.2019.09.035.
  2. ^ a b Santomauro, Giuseppe; Alderuccio, Daniela; Ambrosino, Fiorenzo; Migliori, Silvio (2021). "Ranking Cryptocurrencies by Brand Importance: A Social Media Analysis in ENEAGRID". In Bitetta, Valerio; Bordino, Ilaria; Ferretti, Andrea; Gullo, Francesco; Ponti, Giovanni; Severini, Lorenzo (eds.). Mining Data for Financial Applications. Lecture Notes in Computer Science. Vol. 12591. Cham: Springer International Publishing. pp. 92–100. doi:10.1007/978-3-030-66981-2_8. ISBN 978-3-030-66981-2.
  3. ^ a b c d Bashar, Md Abul; Nayak, Richi; Balasubramaniam, Thirunavukarasu (2022-07-25). "Deep learning based topic and sentiment analysis: COVID19 information seeking on social media". Social Network Analysis and Mining. 12 (1): 90. doi:10.1007/s13278-022-00917-5. ISSN 1869-5469. PMC 9312316. PMID 35911483.
  4. ^ Keller, Kevin Lane (1993). "Conceptualizing, Measuring, and Managing Customer-Based Brand Equity". Journal of Marketing. 57 (1): 1–22. doi:10.1177/002224299305700101. ISSN 0022-2429.
  5. ^ Fronzetti Colladon, Andrea (2018). "The Semantic Brand Score". Journal of Business Research. 88: 150–160. arXiv:2105.05781. doi:10.1016/j.jbusres.2018.03.026.
  6. ^ a b c Bianchino, Antonella; Fusco, Daniela; Pisciottano, Daniele (2021-05-27). "How to Measure the Touristic Competitiveness: A Mixed Mode Model Proposal" (PDF). Athens Journal of Tourism. 8 (2): 131–146. doi:10.30958/ajt.8-2-4.
  7. ^ a b c Beccari, Nicholas; Nicola, Valerio (2019). Brand-generated and Usergenerated content videos on YouTube: characteristics, behavior and user perception (PDF). Milan, Italy: Politecnico di Milano.
  8. ^ Indraccolo, Ugo; Losavio, Ernesto; Carone, Mauro (2023). "Applying graph theory to improve the quality of scientific evidence from textual information: Neural injuries after gynaecologic pelvic surgery for genital prolapse and urinary incontinence". Neurourology and Urodynamics. 42 (3): 669–679. doi:10.1002/nau.25133. ISSN 0733-2467. PMID 36648454.
  9. ^ "Polish Twitter on immigrants during the 2021 Belarus–European Union border crisis". www.linkedin.com. Retrieved 2024-04-03.
  10. ^ a b Das, Sibanjan Debeeprasad; Bala, Pradip Kumar; Das, Sukanta (2024). "Exploiting User-Generated Content in Product Launch Videos to Compute a Launch Score". IEEE Access. 12: 49624–49639. Bibcode:2024IEEEA..1249624D. doi:10.1109/ACCESS.2024.3381541. ISSN 2169-3536.
  11. ^ Perkins, Jacob; Fattohi, Faiz (2014). Python 3 text processing with NLTK 3 cookbook. Quick answers to common problems (2nd ed.). Birmingham: Packt Publishing Ltd. ISBN 978-1-78216-785-3.
  12. ^ Colladon, Andrea Fronzetti; Naldi, Maurizio (2020-05-22). "Distinctiveness centrality in social networks". PLOS ONE. 15 (5): e0233276. arXiv:1912.03391. Bibcode:2020PLoSO..1533276F. doi:10.1371/journal.pone.0233276. ISSN 1932-6203. PMC 7244137. PMID 32442196.
  13. ^ Bashar, Md Abul; Nayak, Richi; Knapman, Gareth; Turnbull, Paul; Fforde, Cressida (December 2023). "An Informed Neural Network for Discovering Historical Documentation Assisting the Repatriation of Indigenous Ancestral Human Remains". Social Science Computer Review. 41 (6): 2293–2317. arXiv:2303.14475. doi:10.1177/08944393231158788. ISSN 0894-4393.
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