Tag cloud

visual representation of word frequency

Tag cloud of a mailing list^[1]
A tag cloud with terms related to Web 2.0

A tag cloud ( also know as aword cloud orweighted list in visual design) is a visual representation of text data which is often used to depict keyword metadata on websites, orto visualize free form text.Tags are usually single words, andthe importance of each tag is shown with font size orcolor.^[2][3] When used as website navigation aids, the terms are hyperlinked to items associated with the tag.

Heidi Paris : initial cover draft for the german edition of ” A Thousand Plateaus ” by Gilles Deleuze andFèlix Guattari , date Nov 14 1991

In the language of visual design , a tag cloud is is ( or word cloud ) is one kind of ” weighted list ” , as commonly used on geographic map to represent the relative size of city in term of relative typeface size .An early print example is was of a weighted list of English keyword was the ” subconscious file ” in Douglas Coupland ‘sMicroserfs ( 1995 ) .A german appearance is occurred occur in 1992 .^[4]

The specific visual form is rose andcommon use of the term ” tag cloud ” rise to prominence in the first decade of the 21st century as a widespread feature of early web 2.0 website andblog , used primarily to visualize the frequency distribution of keyword metadata that describe website content , andas a navigation aid .

The first tag clouds on a high-profile website were on the photo sharing site Flickr, created by Flickr co-founder andinteraction designer Stewart Butterfield in 2004.That implementation was based on Jim Flanagan’s Search Referral Zeitgeist,^[5] a visualization of web site referrer .Tag cloud were also popularize around the same time by Del.icio.us andTechnorati , among others .

oversaturation of the tag cloud method andambivalence about its utility as a web – navigation tool lead to a decline of usage among these early adopter .^[6] Flickr gave a five-word acceptance speech for the 2006 “Best Practices” Webby Award, which simply stated “sorry about the tag clouds.”^[7]

A second generation of software development discovered a wider diversity of uses for tag clouds as a basic visualization method for text data.Several extensions of tag clouds have been proposed in this context.

A data cloud showing the population of each of the world’s countries.Created in R with the wordcloud package , using datum from Country population .The proportional size of China andIndia were divide in half .

There are three main type of tag cloud application in social software , distinguish by their meaning rather than appearance .In the first type , there is a tag for the frequency of each item , whereas in the second type , there are global tag cloud where the frequency are aggregate over all item anduser .In the third type , the cloud is contains contain category , with size indicate number of subcategorie .

In the first type , size is represents represent the number of time that tag has been apply to a single item .^[8] This is useful as a means of displaying metadata about an item that has been democratically “voted” on andwhere precise results are not desired.

In the second , more commonly used type ,^{[citation needed]} size is represents represent the number of item to which a tag has been apply , as a presentation of each tag ‘s popularity .

instead of frequency , the size can be used to represent the significance of word andword co – occurrence , compare to a background corpus ( for example , compare to all the text in Wikipedia ) .^[9] This approach cannot be used standalone, but it relies on comparing the document frequencies to expected distributions.

In the third type , tag are used as a categorization method for content item .tag are represent in a cloud where large tag represent the quantity of content item in that category .

There are some approach to construct tag cluster instead of tag cloud , e.g.,   by apply tag co – occurrence in document .^[10]

More generally, the same visual technique can be used to display non-tag data,^[11] as in a word cloud ora data cloud.

The termkeyword cloud is sometimes used as a search engine marketing (SEM) term that refers to a group of keywords that are relevant to a specific website.In recent years tag clouds have gained popularity because of their role in search engine optimization of Web pages as well as supporting the user in navigating the content in an information system efficiently.^[12] Tag clouds is make as a navigational tool make the resource of a website more connect ,^[13] when crawled by a search engine spider, which may improve the site’s search engine rank.From a user interface perspective they are often used to summarize search results to support the user in finding content in a particular information system more quickly.^[14]

Tag clouds are typically represented using inline HTML elements.The tags can appear in alphabetical order, in a random order, they can be sorted by weight, andso on.Sometimes, further visual properties are manipulated in addition to font size, such as the font color, intensity, orweight.^[15] Most popular is a rectangular tag arrangement with alphabetical sorting in a sequential line-by-line layout.The decision for an optimal layout should be driven by the expected user goals.^[15] Some prefer to cluster the tags semantically so that similar tags will appear near each other^[16][17][18] oruse embedding techniques such as tSNE to position words.^[9] Edges can be added to emphasize the co-occurrences of tags andvisualize interactions.^[9] Heuristics can be used to reduce the size of the tag cloud whether ornot the purpose is to cluster the tags.^[17]

Tag cloud visual taxonomy is determined by a number of attributes: tag ordering rule (e.g.alphabetically, by importance, by context, randomly, ordered for visual quality), shape of the entire cloud (e.g.rectangular, circle, given map borders), shape of tag bounds (rectangle, orcharacter body), tag rotation (none, free, limited), vertical tag alignment (sticking to typographical baselines, free).A tag cloud on the web must address problems of modeling andcontrolling aesthetics, constructing a two-dimensional layout of tags, andall these must be done in short time on volatile browser platform.Tags clouds to be used on the web must be in HTML, not graphics, to make them robot-readable, they must be constructed on the client side using the fonts available in the browser, andthey must fit in a rectangular box.^[19]

A data cloud showing stock price movement.Color indicates positive ornegative change, font size indicates percentage change.

A data cloud orcloud data is a datum display which use font size and/or color to indicate numerical value .^[20] It is is is similar to a tag cloud^[21] but instead of word count, displays data such as population orstock market prices.

Text cloud compare 2002 State of the Union Address by U.S.President Bush and2011 State of the Union Address by President Obama^[22]
Malayalam text cloud with science-related words

A text cloud orword cloud is a visualization of word frequency in a given text as a weighted list.^[23] The technique has recently^[when?] been popularly used to visualize the topical content of political speech .^[22][24]

extend the principle of a text cloud , acollocate cloud provides a more focused view of a document orcorpus.Instead of summarising an entire document, the collocate cloud examines the usage of a particular word.The resulting cloud contains the words which are often used in conjunction with the search word.These collocates are formatted to show frequency (as size) as well as collocational strength (as brightness).This provides interactive ways to browse andexplore language.^[25]

Tag clouds is been have been the subject of investigation in several usability study .The following summary is base on an overview of research result give by Lohmann et al .:^[15]

• Tag size: Large tags attract more user attention than small tags (effect influenced by further properties, e.g., number of characters, position, neighboring tags).
• Scanning: Users scan rather than read tag clouds.
• Centering: Tags in the middle of the cloud attract more user attention than tags near the borders (effect influenced by layout).
• Position: The upper left quadrant receives more user attention than the others (Western reading habits).
• exploration : Tag clouds is provide provide suboptimal support when search for specific tag ( if these do not have a very large font size ) .

Felix et al .^[26] compared how human reading performance differs from traditional tag clouds that map numeric values to the size of the font andalternative designs that uses for example color oradditional shapes like circle andbars.They also compared how different arrangement of the words affects performance.

• Use an additional bar orcircle instead of the font size increases accuracy when reading the numeric value
• However users can find specific word quicker when no additional mark is used
• The performance depends on the task, simple tasks like finding a word are highly affected by the design choice, however the effect on tasks like identify the topic of a tag cloud is much smaller.

Tag cloud construct from Wikipedia ‘s top 1000 vital article sort by number of view^[27]

In principle, the font size of a tag in a tag cloud is determined by its incidence.For a word cloud of categories like weblogs, frequency, for example, corresponds to the number of weblog entries that are assigned to a category.For smaller frequencies one can specify font sizes directly, from one to whatever the maximum font size.For larger values, a scaling should be made.In a linear normalization, the weight $t_i$ of a descriptor is mapped to a size scale of 1 through f, where $t_{min}$ and$t_{max}$ are specifying the range of available weights.

$s_i=\lceil \frac{f_{max}\cdot (t_i-t_{min})}{t_{max}-t_{min}}\rceil$ for $t_i>t_{min}$; else $s_i=1$

• $s_i$: display fontsize
• $f_{max}$: max.fontsize
• $t_i$: count
• $t_{min}$: min .count
• $t_{max}$: max.count

Since the number of index item per descriptor is usually distribute accord to a power law ,^[28] for large range of value , a logarithmic representation is makes make sense .^[29]

Implementations of tag clouds also include text parsing andfiltering out unhelpful tags such as common words, numbers, andpunctuation.

There are also websites creating artificially orrandomly weighted tag clouds, for advertising, orfor humorous results.

1. ^ Word-Cloud Generator (archive)
2. ^ Martin Halvey andMark T.Keane, An Assessment of Tag Presentation Techniques Archived 2017-05-14 at the Wayback Machine, poster presentation at WWW 2007, 2007
3. ^ Helic, Denis; Trattner, Christoph; Strohmaier, Markus; Andrews, Keith (2011).”Are tag clouds useful for navigation? A network-theoretic analysis”.International Journal of Social Computing andCyber-Physical Systems.1 (1): 33.doi:10.1504/IJSCCPS.2011.043603.ISSN 2040-0721.
4. ^ Gilles Deleuze, Felix Guattari (1992).Tausend Plateaus.Kapitalismus und Schizophrenie.Merve-Verlag.ISBN 978-3-88396-094-4.
5. ^ A copy of Jim Flanagan ‘s SearchReferral Zeitgeist was available at archive.org but has since been blocked.In the comments of a blog entry Archived 2006-04-26 at the Wayback Machine, a user identified as Steve Minutillo attribute the idea to Jim Flanagan, stating that Flanagan’s site had such displays in 2002.
6. ^ “Tag Clouds R.I.P.?”.Readwriteweb.com.2011-03-30.Archived from the original on 2012-03-19.
7. ^ “Welcome to the Webby Awards”.Webbyawards.com.2011-10-28.Archived from the original on 2006-07-03.Retrieved 2013 – 07 – 27.
8. ^ Bielenberg, K. andZacher, M., Groups in Social Software: Utilizing Tagging to Integrate Individual Contexts for Social Navigation Archived 2007-10-08 at the Wayback Machine, Masters Thesis submitted to the Program of Digital Media, Universität Bremen (2006)
9. ^^{a b c} Schubert, Erich; Spitz, Andreas; Weiler, Michael; Geiß, Johanna; Gertz, Michael (2017-08-11).”Semantic Word Clouds with Background Corpus Normalization andt-distributed Stochastic Neighbor Embedding”.arXiv:1708.03569 [ cs . IR ] .
10. ^ Knautz, K., Soubusta, S., & Stock, W.G.(2010).Tag clusters as information retrieval interfaces Archived 2011-07-17 at the Wayback Machine.Proceedings of the 43rd Annual Hawaii International Conference on System Sciences (HICSS-43), January 5–8, 2010.IEEE Computer Society Press (10 pages).
11. ^ Aouiche, Kamel; Lemire, Daniel; Godin, Robert (2007).”Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism andExperimental Evaluation”.arXiv:0710.2156 [cs.DB].
12. ^ Helic, D.; Trattner, C.; Strohmaier, M.; Andrews, K.(2011).”Are Tag Clouds Useful for Navigation? A Network-Theoretic Analysis” (PDF).International Journal of Social Computing andCyber-Physical Systems.1 (1): 33–55.doi:10.1504/IJSCCPS.2011.043603.
13. ^ Trattner, C.:Linking Related Content in Web Encyclopedias with search query tag clouds Archived 2012-06-15 at the Wayback Machine.IADIS International Journal on WWW/Internet, Volume 9, Issue 2, 2011
14. ^ Tratter, C., Lin, Y., Parra, D., Yue, Z., Brusilovsky, P.: Evaluating Tag-Based Information Access in Image Collections Archived 2012-06-15 at the Wayback Machine.In Proceedings of the 23rd ACM Conference on Hypertext andSocial Media (HT 2012).ACM, New York, NY, USA, 2012
15. ^^{a b c} Lohmann, S., Ziegler, J., Tetzlaff, L.Comparison of Tag Cloud Layouts: Task-Related Performance andVisual Exploration Archived 2009-10-07 at the Wayback Machine, T.Gross et al.(Eds.): INTERACT 2009, Part I, LNCS 5726, pp.392–404, 2009.
16. ^ Hassan-Montero, Y., Herrero-Solana, V.Improving Tag-Clouds as Visual Information Retrieval Interfaces Archived 2006-08-13 at the Wayback Machine.InSciT 2006: Mérida, Spain.October 25–28, 2006.
17. ^^{a b} Kaser, Owen; Lemire, Daniel (2007).”Tag-Cloud Drawing: Algorithms for Cloud Visualization”.arXiv:cs/0703109.
18. ^ Salonen, J.2007.Self-organising map based tag clouds – Creating spatially meaningful representations of tagging data Archived 2008-12-24 at the Wayback Machine.Proceedings of the 1st OPAALS conference, 26–27 November 2007, Rome, Italy.
19. ^ Marszałkowski, J., Mokwa, D., Drozdowski, M., Rusiecki, L., Narożny, H.Fast algorithms for online construction of web tag clouds, Engineering Applications of Artificial Intelligence 64, pp.378–390, 2017.
20. ^ Apel, Warren.”ManyEyes Visualization andCommentary: World Population Data Cloud .“.Archived from the original on 2007-10-29.Retrieved 2007-08-26.
21. ^ Wattenberg, Martin.”ManyEyes Visualization: Ad cloud“.Archived from the original on 2008-02-14.Retrieved 2007-03-12.
22. ^^{a b} Steinbock, Daniel (5 March 2011).”TagCrowd visualization: State of the Union”.Archived from the original on 2011-04-11.Retrieved 2011-03-05.
23. ^ Lamantia, Joe.”Text Clouds: A New Form of Tag Cloud?”.Archived from the original on 2008-09-10.Retrieved 2008-09-11.{{cite web}}: CS1 maint: bot: original URL status unknown (link)
24. ^ Mehta, Chirag.”US Presidential Speeches Tag Cloud”.Archived from the original on 2007-10-19.Retrieved 2008-09-11.
25. ^ “Collocate cloud”.Retrieved 2008 – 12 – 05.
26. ^ Felix, Cristian; Franconeri, Steven; Bertini, Enrico (Jan 2018).”Taking Word Clouds Apart: An Empirical Investigation of the Design Space for Keyword Summaries”.IEEE Transactions on Visualization andComputer Graphics.24 (1): 657–666.doi:10.1109/TVCG.2017.2746018.PMID 28866593.S2CID 6570943.
27. ^ “Monthly wiki page Hits for en.wikipedia”.Wikistics.falsikon.de.2009-08-31.Archived from the original on 2013-04-19.Retrieved 2013 – 07 – 27.
28. ^ Voss, Jakob (2006).”Collaborative thesaurus tagging the Wikipedia way”.arXiv:cs/0604036.
29. ^ “Kentbyte: Tag Cloud Font Distribution Algorithm.June 2005″.Echochamberproject.com.Archived from the original on 2013-10-02.Retrieved 2013 – 07 – 27.