View source for Draft:Semantic Brand Score

{{Short description|Measure of brand importance}}
{{Draft topics|stem}}
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{{AfC comment|{{Olive|Stemming and sources.}} Stemming of words like "golden" does not remove word affixes because of the Porter's stemming algorithm. It can be tested here http://text-processing.com/demo/stem/ and here https://9ol.es/porter_js_demo.html. 

Added additional secondary sources well describing the metric.
This article is significantly different than the one previously deleted (and with much more secondary sources).
[[User:WarmKomorebi|WarmKomorebi]] ([[User talk:WarmKomorebi|talk]]) 09:30, 18 October 2024 (UTC)}}{{AFC comment|1={{Olive|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 [sic] used to refine texts, involving tasks like eliminating stopwords and word affixes through stemming.}} The proximity in the illustration is not one sentence but three significant words. And it is curious that the "e" of ''purple'' is -- I infer -- an "affix" (to allow for ''purplish'', etc?) but the "en" of ''golden'' is not. 

Perhaps good sources on the "Semantic Brand Score" put this more convincingly. [[User:Hoary|Hoary]] ([[User talk:Hoary|talk]]) 23:41, 30 September 2024 (UTC)}}

{{AFC comment|1=[[Wikipedia:Articles for deletion/Semantic Brand Score|This discussion]] resulted in the articles deletion, it is similar to the deleted version. [[User:Geardona|Geardona]] ([[User talk:Geardona|talk to me?]]) 22:04, 3 April 2024 (UTC)}}

{{AFC comment|1=Outside of the lede, is un-cited, please add references to the section. [[User:Geardona|Geardona]] ([[User talk:Geardona|talk to me?]]) 21:57, 3 April 2024 (UTC)}}

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The '''Semantic Brand Score''' (SBS) is a measure of brand importance that is calculated on textual data<ref name=":0">{{Cite journal |last1=Schlaile |first1=Michael P. |last2=Bogner |first2=Kristina |last3=Muelder |first3=Laura |date=2021 |title=It's more than complicated! Using organizational memetics to capture the complexity of organizational culture |url=https://linkinghub.elsevier.com/retrieve/pii/S014829631930582X |journal=Journal of Business Research |language=en |volume=129 |pages=801–812 |doi=10.1016/j.jbusres.2019.09.035}}</ref><ref name=":4">{{Cite book |last1=Santomauro |first1=Giuseppe |last2=Alderuccio |first2=Daniela |last3=Ambrosino |first3=Fiorenzo |last4=Migliori |first4=Silvio |chapter=Ranking Cryptocurrencies by Brand Importance: A Social Media Analysis in ENEAGRID |series=Lecture Notes in Computer Science |date=2021 |volume=12591 |editor-last=Bitetta |editor-first=Valerio |editor2-last=Bordino |editor2-first=Ilaria |editor3-last=Ferretti |editor3-first=Andrea |editor4-last=Gullo |editor4-first=Francesco |editor5-last=Ponti |editor5-first=Giovanni |editor6-last=Severini |editor6-first=Lorenzo |title=Mining Data for Financial Applications |chapter-url=https://link.springer.com/chapter/10.1007/978-3-030-66981-2_8 |language=en |location=Cham |publisher=Springer International Publishing |pages=92–100 |doi=10.1007/978-3-030-66981-2_8 |isbn=978-3-030-66981-2}}</ref><ref name=":1">{{Cite journal |last1=Bashar |first1=Md Abul |last2=Nayak |first2=Richi |last3=Balasubramaniam |first3=Thirunavukarasu |date=2022-07-25 |title=Deep learning based topic and sentiment analysis: COVID19 information seeking on social media |url=https://doi.org/10.1007/s13278-022-00917-5 |journal=Social Network Analysis and Mining |language=en |volume=12 |issue=1 |pages=90 |doi=10.1007/s13278-022-00917-5 |issn=1869-5469 |pmc=9312316 |pmid=35911483}}</ref>. The measure is rooted in [[graph theory]] and partly connected to Keller's<ref>{{Cite journal |last=Keller |first=Kevin Lane |date=1993 |title=Conceptualizing, Measuring, and Managing Customer-Based Brand Equity |url=http://journals.sagepub.com/doi/10.1177/002224299305700101 |journal=Journal of Marketing |language=en |volume=57 |issue=1 |pages=1–22 |doi=10.1177/002224299305700101 |issn=0022-2429}}</ref> conceptualization of brand equity<ref name=":6">{{Cite journal |last=Fronzetti Colladon |first=Andrea |date=2018 |title=The Semantic Brand Score |url=https://linkinghub.elsevier.com/retrieve/pii/S0148296318301541 |journal=Journal of Business Research |language=en |volume=88 |pages=150–160 |doi=10.1016/j.jbusres.2018.03.026|arxiv=2105.05781 }}</ref>. The metric has been computed by examining different text sources, such as newspaper articles, online forums, scientific papers, or [[social media]] posts<ref>{{Cite journal |last1=Indraccolo |first1=Ugo |last2=Losavio |first2=Ernesto |last3=Carone |first3=Mauro |date=2023 |title=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 |url=https://onlinelibrary.wiley.com/doi/10.1002/nau.25133 |journal=Neurourology and Urodynamics |language=en |volume=42 |issue=3 |pages=669–679 |doi=10.1002/nau.25133 |issn=0733-2467 |pmid=36648454}}</ref><ref>{{Cite news |last=Kasia |first=Parys |title=Polish Twitter on immigrants during the 2021 Belarus–European Union border crisis |url=https://www.linkedin.com/pulse/polish-twitter-immigrants-during-2021-belaruseuropean-kasia-parys |access-date=2024-04-03 |website=www.linkedin.com |language=en}}</ref><ref name=":5">{{Cite journal |last1=Das |first1=Sibanjan Debeeprasad |last2=Bala |first2=Pradip Kumar |last3=Das |first3=Sukanta |date=2024 |title=Exploiting User-Generated Content in Product Launch Videos to Compute a Launch Score |url=https://ieeexplore.ieee.org/document/10478487 |journal=IEEE Access |volume=12 |pages=49624–49639 |bibcode=2024IEEEA..1249624D |doi=10.1109/ACCESS.2024.3381541 |issn=2169-3536}}</ref>.

== Definition and calculation ==

=== Pre-processing ===
To compute the Semantic Brand Score, it is necessary to convert the analyzed texts into [[Co-occurrence network|word networks]], i.e., graphs where each node signifies a word. Connections between words are formed based on their co-occurrence within a specified distance threshold (a number of words). [[Natural language processing|Natural language pre-processing]] is usually conducted to refine texts, which involves tasks such as removing [[Stop word|stopwords]] and applying [[stemming]]<ref>{{Cite book |last1=Perkins |first1=Jacob |title=Python 3 text processing with NLTK 3 cookbook |last2=Fattohi |first2=Faiz |date=2014 |publisher=Packt Publishing Ltd |isbn=978-1-78216-785-3 |edition=2nd |series=Quick answers to common problems |location=Birmingham}}</ref> to eliminate word affixes.  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".

[[File:Word co-occurrence network (range 3 words) - ENG.jpg|frameless|500x500px]]

The SBS is a composite indicator with three dimensions: prevalence, diversity and connectitivy<ref name=":2">{{Cite journal |last1=Bianchino |first1=Antonella |last2=Fusco |first2=Daniela |last3=Pisciottano |first3=Daniele |date=2021-05-27 |title=How to Measure the Touristic Competitiveness: A Mixed Mode Model Proposal |url=https://www.athensjournals.gr/tourism/2021-8-2-4-Bianchino.pdf |journal=Athens Journal of Tourism |volume=8 |issue=2 |pages=131–146 |doi=10.30958/ajt.8-2-4}}</ref><ref name=":3">{{Cite book |last1=Beccari |first1=Nicholas |url=https://www.politesi.polimi.it/retrieve/a81cb05d-7655-616b-e053-1605fe0a889a/Thesis.pdf |title=Brand-generated and Usergenerated content videos on YouTube: characteristics, behavior and user perception |last2=Nicola |first2=Valerio |date=2019 |publisher=Politecnico di Milano |location=Milan, Italy}}</ref><ref name=":7">{{cite book |last=Mercurio |first=Simona |date=2024 |editor-last1=Giordano |editor-first1=Giuseppe |editor-last2=Misuraca |editor-first2=Michelangelo |title=New Frontiers in Textual Data Analysis |publisher=Springer |pages=349–359 |chapter=What About Corruption? A Text Analytics Method for a Scoping Literature Review |series=Studies in Classification, Data Analysis, and Knowledge Organization |doi=10.1007/978-3-031-55917-4_28 |chapter-url=https://link.springer.com/chapter/10.1007/978-3-031-55917-4_28 |isbn=978-3-031-55916-7}}</ref>. SBS measures brand importance, a construct that cannot be understood by examining a single dimension alone<ref name=":6" />.

=== Prevalence ===
Prevalence measures the frequency of brand name usage, indicating how often a brand is explicitly referenced in a [[Text corpus|corpus]]. The prevalence factor is associated with [[brand awareness]], suggesting that a brand mentioned frequently in a text is more familiar to its authors<ref name=":2" /><ref name=":3" /><ref name=":5" />. Likewise, frequent mentions of a brand name enhance its recognition and recall among readers.

=== Diversity ===
Diversity 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 or term. Measurement involves employing the [[degree centrality]] indicator, reflecting the number of connections a brand node has in the semantic network<ref name=":0" />. Alternatively, an approach using [[distinctiveness centrality]]<ref>{{Cite journal |last1=Colladon |first1=Andrea Fronzetti |last2=Naldi |first2=Maurizio |date=2020-05-22 |title=Distinctiveness centrality in social networks |journal=PLOS ONE |language=en |volume=15 |issue=5 |pages=e0233276 |doi=10.1371/journal.pone.0233276 |doi-access=free |issn=1932-6203 |pmc=7244137 |pmid=32442196|arxiv=1912.03391 |bibcode=2020PLoSO..1533276F }}</ref> 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 <math>w_{ij}=0</math>, otherwise the weight of the arc connecting them is <math>w_{ij} \ge 1</math>. 
In the following, <math>g_j</math> is the degree of node ''j'' and <math>I_{(f)}</math> is the indicator function which equals 1 if <math>f=TRUE</math>, i.e. if there is an arc connecting nodes ''i'' and ''j''.

<math>DI (i) = \sum_{j=1,j\neq i}^{n}\log_{10}\frac{n-1}{g_{j}}I_{(w_{ij}>0)}</math>.

=== Connectivity ===
Connectivity evaluates a brand's connective power within broader discourse, indicating its capacity to serve as a bridge between various words/concepts (nodes) in the network<ref name=":0" /><ref name=":4" /><ref name=":1" /><ref name=":7" />. 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<ref name=":1" />.<ref>{{Cite journal |last1=Bashar |first1=Md Abul |last2=Nayak |first2=Richi |last3=Knapman |first3=Gareth |last4=Turnbull |first4=Paul |last5=Fforde |first5=Cressida |date=December 2023 |title=An Informed Neural Network for Discovering Historical Documentation Assisting the Repatriation of Indigenous Ancestral Human Remains |url=http://journals.sagepub.com/doi/10.1177/08944393231158788 |journal=Social Science Computer Review |language=en |volume=41 |issue=6 |pages=2293–2317 |doi=10.1177/08944393231158788 |arxiv=2303.14475 |issn=0894-4393}}</ref>

The Semantic Brand Score indicator is given by the sum of the [[standardized]] values of prevalence, diversity, and connectivity<ref name=":0" /><ref name=":2" /><ref name=":3" />. SBS standardization is typically performed by subtracting the mean from the raw scores of each dimension and then dividing by the standard deviation <ref name=":1" />. This process takes into account the scores of all relevant words in the corpus. 

== See also ==

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

== References ==
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{{reflist}}

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

{{Draft categories|
[[:Category:Graph algorithms]]
[[:Category:Graph theory]]
[[:Category:Network analysis]]
[[:Category:Text mining]]
[[:Category:Brand management]]
[[:Category:Network theory]]
[[:Category:Brand valuation]]
}}