Mining tag clouds and emoticons behind community feedback

Ganesan, Kavita; Sundaresan, Neelakantan; Deo, Harshal

doi:10.1145/1367497.1367716

Cited by 18 publications

(9 citation statements)

References 3 publications

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“…Lee et al [16] developed a system in which the user can manually add tags and their sentiment to an entity, and the rated valence of the tag is shown by the font. The system by Ganesan et al [9] uses a tag cloud interface with emoticons to visualize eBay seller feedback (e.g., automatically adding a smiley face to a positive tag). Yatani et al developed Review Spotlight [27] using adjective-noun word pairs in a tag cloud interface.…”

Section: Related Workmentioning

confidence: 99%

ReviewCollage

Jin

Sakai

Yatani

2014

Proceedings of the 16th International Conference on Human-Computer Interaction With Mobile Devices &Amp; Services

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Review comments posted in online websites can help the user decide a product to purchase or place to visit. They can also be useful to closely compare a couple of candidate entities. However, the user may have to read different webpages back and forth for comparison, and this is not desirable particularly when she is using a mobile device. We present ReviewCollage, a mobile interface that aggregates information about two reviewed entities in a one-page view. ReviewCollage uses attribute-value pairs, known to be effective for review text summarization, and highlights the similarities and differences between the entities. Our user study confirms that ReviewCollage can support the user to compare two entities and make a decision within a couple of minutes, at least as quickly as existing summarization interfaces. It also reveals that ReviewCollage could be most useful when two entities are very similar.

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Section: Related Workmentioning

confidence: 99%

ReviewCollage

Jin

Sakai

Yatani

2014

Proceedings of the 16th International Conference on Human-Computer Interaction With Mobile Devices &Amp; Services

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“…For example, Tag Clouds are actively used in many systems (e.g. [26,46,18,28,27]) and are intended to provide a high level view of a document space. However, evidence suggests that while they may be aesthetically pleasing, from a content consumption perspective they are unusable, misrepresentative, and inferior to simple sorted lists [21,26,36,19].…”

Section: Reading Documents In Contextmentioning

confidence: 99%

Echo

Hailpern

Huberman

2013

Proceedings of the 5th ACM SIGCHI Symposium on Engineering Interactive Computing Systems

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The explosive growth of user generated content, along with the continuous increase in the amount of traditional sources of content, has made it extremely hard for users to digest the relevant pieces of information that they need to pay attention to in order to make sense of their needs. Thus, solutions are needed to help both professionals (e.g lawyers, analysts, economists) and ordinary users navigate this flood of information. We present a novel interaction model and system called Echo which uses machine learning techniques to traverse a corpus of documents and distill crucial opinions from the collective intelligence of the crowd. Based on this analysis, Echo creates an intuitive and elegant interface, as though constructed by an editor, that allows users to quickly find salient documents and opinions, all powered by the wisdom of the crowd. The Echo UI directs the user's attention to critical opinions using a natural magazine style metaphor, with visual call outs and other typographic changes. Therefore, this paper present two key contributions (an algorithm and interaction model) that allow a user to "read as normal," while focusing her attention on the important opinions within documents, and showing how these opinions relate to those of the crowd.

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“…Lee et al developed a system in which the user can manually add tags to an entity, and can rate whether the added tag contains a positive or negative sentiment [16]; the rated positivity/negativity of the tag is visualized using the font. Ganesan et al incorporated emoticons into a tag cloud visualizing eBay seller feedback [9]. For example, a smiley face is automatically added to a tag that their system recognized as a positive tag.…”

Section: User Interfaces For User Review Summarizationmentioning

confidence: 99%

Review spotlight

Yatani

Novati

Trusty

et al. 2011

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Many people read online reviews written by other users to learn more about a product or venue. However, the overwhelming amount of user-generated reviews and variance in length, detail and quality across the reviews make it difficult to glean useful information. In this paper, we present the iterative design of our system, called Review Spotlight. It provides a brief overview of reviews using adjective-noun word pairs, and allows the user to quickly explore the reviews in greater detail. Through a laboratory user study which required participants to perform decision making tasks, we showed that participants could form detailed impressions about restaurants and decide between two options significantly faster with Review Spotlight than with traditional review webpages.

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Mining tag clouds and emoticons behind community feedback

Abstract: In this paper we describe our mining system which automatically mines tags from feedback text in an eCommerce scenario. It renders these tags in a visually appealing manner. Further, emoticons are attached to mined tags to add sentiment to the visual aspect.

Cited by 18 publications

References 3 publications

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