Tag Suggestion Research Articles

SEO Ministration : Content Related Tag Suggestion Using One vs Rest

SEO Analyst pays a lot of time finding relevant tags for their articles and in some cases, they are unaware of the content topics. The current proposed ML model will recommend content-related tags so that the Content writers/SEO analyst will be having an overview regarding the content and minimizes their time spent on unknown articles. Machine Learning algorithms have a plethora of applications and the extent of their real-life implementations cannot be estimated. Using algorithms like One vs Rest (OVR), Long Short-Term Memory (LSTM), this study has analyzed how Machine Learning can be useful for tag suggestions for a topic. The training of the model with One vs Rest turned out to deliver more accurate results than others. This Study certainly answers how One vs Rest is used for tag suggestions that are needed to promote a website and further studies are required to suggest keywords required.

Medical Quora Tagging using MATAR and LDA Algorithm

The success of clustering or classification methods the detection of relevant textual formats is incredibly meaningful. The high dimensionality and irrelevance of textual materials was subjected to text records. Existing methods lack integration and are particularly vulnerable to original value. Metaheuristic algorithms are also applied to solve the challenges of standard classification algorithms. In this paper, an enhanced Latent Dirichlet Assignment clustering method & Inter Modeling for Tag Suggestion rating system is documented to boost correlation - based & identification efficiency to suggest labels with material modern web labels that promotes the exchange of medical information using unmonitored data through question-answering. For accurate tagging, Methods like POS marking, Hopping, Whistles& Stopping words are being used for speech recognition. The efficiency of the evolved architectures is compared to the standard methods, by using specificity of the recommendation, defining features, sensitivity, plain word and speed. The findings reveal that the classification and grouping scheme of the proposed structure succeeds traditional textual record approaches.

The effect of multilingual suggested tags on cross-language information tagging behaviour

Purpose Suggested tag was considered as one of the critical factors affecting a user’s tagging behaviour. However, compared to the findings on the suggested tags for the monolingual environment, it still lacks focused studies on the tag suggestions for cross-language information. Therefore, this paper aims to concern with annotation behaviour and psychological cognition in the cross-language environment when suggested tags are provided. Design/methodology/approach A cross-language tagging experiment was conducted to explore the impact of suggested tags on the tagging results and process. The descriptive statistics of tags, the sources and semantic relations of tags, as well as the user’s psychological cognition were all measured in the test. Findings The experimental results demonstrated that the multilingual suggested tags could bring some costs to a user’s tagging perception. Furthermore, the language factor of suggested tags led to different paths of tagging imitation (reflected by longer semantic mapping and imitation at the visual level) and different cognitive processes (topic extraction and inference process). Originality/value To the best of the authors’ knowledge, this study is one of the first to emphasize the effect of suggested tags during multilingual tagging. The findings will enrich the theories of user-information interaction in the cross-language environment and, in turn, provide practical implications for tag-based information system design.

Multiple Time Series Perceptive Network for User Tag Suggestion in Online Innovation Community

User tag suggestion technique, aiming at learning users’ preferences over knowledge products from their historical behaviors, plays an important role in generating personalized recommendation in online innovation community. However, most current user tagging solutions only utilize a single kind of behavior to predict a single tag for users, resulting in weak generalization of user profile. In this paper, we propose a multiple time series perceptive network (MTSPN) for user tagging tasks in online innovation community. In particular, MTSPN takes multiple kinds of user behaviors into consideration for collaborative perception purpose, in which multi-scale sequential features are extracted from different sequential behaviors, and a multi-label classification module is built-in the proposed MTSPN model to predict multiple tags for users. Our encouraging experimental results on a real-world dataset collected from “Thingiverse” community validate the superiority of the our MTSPN model over several existing user tagging methods.

Recommended System for Enhancing Tag Popularity in a Question Answering Community through Splay-net Techniques

Collaborative filtering filters information by using the recommendations of peer participants. The long tail problem states users with higher points obtain a high reputation compared to less scored users. In popular community question answering websites, like stack exchange network sites, users with unanswered or ignored questions for a long time get a tumbleweed badge without considering their past history. This deteriorates their further contribution to the website. Mostly new or low-reputation people ask the tumbleweed questions. The popularity of the tags follows a long tail theory. The focus of this research work is to design a recommendation system that prevents participants from tumbleweed badge with tag suggestion method to add new or non-popular tags to the existing popular tag list. The splay-net has a self-balancing graph which brings the recently accessed item to the top of the tree. In this paper, we use the splay-net technique to represent users’ reputation along with their tags.

Multi-Label Community-Based Question Classification via Personalized Sequence Memory Network Learning

Multi-label community-based question classification is a challenging problem in Community-based Question Answering (CQA) services, arising in many real applications such as question navigation and expert finding. Most of the existing approaches consider the problem as content-based tag suggestion task, which suffers from the textual sparsity issue. Unlike the previous studies, we consider the problem of multi-label community-based question classification from the viewpoint of personalized sequence learning. We introduce the personalized sequence memory network that leverages not only the semantics of questions but also the personalized information of askers to provide the sequence tag learning function to capture the high-order tag dependency. The experiment on real-world dataset shows the effectiveness of our method.

AutoPrivacy: Automatic privacy protection and tagging suggestion for mobile social photo

With the increasing computing and storage capabilities, smart mobile devices are changing our daily lives and are emerging as the dominant computing platform for end-users. It is popular among the mobile users to take photos including selfies whenever and wherever they like, and further the captured photos are shared to their friends through social networks such as Facebook and WeChat. However, an increasing issue with the large number of photos taken by a mobile user is local photo management, e.g., image searching among the photos without image tags. Another issue is that photo sharing on social networks may infringe on the privacy of the unintended human objects in the images. In this paper, an automatic privacy protection and tag suggestion system, AutoPrivacy, is proposed for mobile social images. In particular, AutoPrivacy attempts to exploit sensors signatures, image processing, and the recognition model to achieve automatic privacy protection for the unintended human objects and tagging suggestion for the intended human objects. We utilize public album data of volunteers from Facebook to test the proposed automatic system, and the experimental results on an Android platform show that AutoPrivacy can perform real time detection of intended/unintended human objects and in turn provide accurate privacy protection for unintended human objects, while the tagging suggestion for the intended human objects is efficient requiring less additional storage.

Entity Annotation WordPress Plugin using TAGME Technology

The development of internet technology makes more information can be accessed. It makes information need to be organized in order to be easily managed. One solution can be used is by using the entity annotation approach which generates tags to represent that document. In this study, TAGME technology is implemented on a WordPress plugin, which is used to manage a blog. Moreover, information on Wikipedia ‘Bahasa Indonesia’ is processed to generate an anchor dictionary which is required by the technology that is implemented. This plugin performs entity annotation by giving tag suggestion for posts in a blog. Testing is carried out by measuring the precision, recall, and of tag suggestions given by the plugin. The result shows that the plugin can give tag suggestions with precision 0.7638, recall 0.5508, and 0.59.

Public faces? A critical exploration of the diffusion of face recognition technologies in online social networks

In recent years, we have witnessed a rapid spread of biometric technologies from the security domain to commercial and social media applications. In this article, we critically explore the repercussions of this diffusion of face recognition to everyday contexts with an in-depth analysis of Facebook’s “tag suggestions” tool which first introduced the technology to on-line social networks. We use Nissenbaum’s framework of contextual integrity to show how the informational norms associated with biometrics in security and policing - their contexts of emergence - are grafted on-line social networks onto their context of iteration. Our analysis reveals a process that has inadvertently influenced the way users understand face recognition, precluding critical questioning of its wider use. It provides an important deepening of contextually-driven approaches to privacy by showing the process through which contexts are co-constitutive of informational norms. Citizens are also offered a critical tool for understanding the trajectory of biometrics and reflect on the data practices associated with the use of face recognition in social media and society at large.

An Integrated Support to Collaborative Semantic Annotation

Everybody experiences every day the need to manage a huge amount of heterogeneous shared resources, causing information overload and fragmentation problems. Collaborative annotation tools are the most common way to address these issues, but collaboratively tagging resources is usually perceived as a boring and time consuming activity and a possible source of conflicts. To face this challenge, collaborative systems should effectively support users in the resource annotation activity and in the definition of a shared view. The main contribution of this paper is the presentation and the evaluation of a set of mechanisms (personal annotations over shared resources and tag suggestions) that provide users with the mentioned support. The goal of the evaluation was to (1) assess the improvement with respect to the situation without support; (2) evaluate the satisfaction of the users, with respect to both the final choice of annotations and possible conflicts; (3) evaluate the usefulness of the support mechanisms in terms of actual usage and user perception. The experiment consisted in a simulated collaborative work scenario, where small groups of users annotated a few resources and then answered a questionnaire. The evaluation results demonstrate that the proposed support mechanisms can reduce both overload and possible disagreement.

Blog Annotation: From Corpus Analysis to Automatic Tag Suggestion

Nowadays, blogs cover a large audience and they raised from the underground to become part of mainstream media. Blogs contain information on diverse topics, personal opinions, and discussions between bloggers and readers. Tags and categories are structural elements of a blog post that increase the blog's visibility, enhance navigation and searching within the blog history. We suppose that those annotations are made on subjective grounds rather than in a systematic way. Even if there are tools to help bloggers to tag and categorize their posts, we still don't know to which extent these tools take into account information contained in previous posts. This paper presents a 11 million word corpus of blogs posts in French dedicated to study these questions, and an experiment in tag and category prediction. Preliminary results show that around 27% of the overall tags can be predicted from lexical frequency analysis of blog posts. However, a first comparison experience with an existing tag suggestion tool shows that an important proportion of the tags used for blog description are not present in the blog post. This shows that tag suggestion tools should exploit the diachronic analysis of blogs.

Where are the ‘key’ words? Optimizing multimedia textual attributes to improve viewership

Multimedia content is central to our experience on the Web. Specifically, users frequently search and watch videos online. The textual features that accompany such content (e.g., title, description, and tags) can generally be optimized to attract more search traffic and ultimately to increase the advertisement-generated revenue.This study investigates whether automating tag selection for online video content with the goal of increasing viewership is feasible. In summary, it shows that content producers can lower their operational costs for tag selection using a hybrid approach that combines dedicated personnel (often known as ‘channel managers’), crowdsourcing, and automatic tag suggestions. More concretely, this work provides the following insights: first, it offers evidence that existing tags for a sample of YouTube videos can be improved; second, this study shows that an automated tag recommendation process can be efficient in practice; and, finally it explores the impact of using information mined from various data sources associated with content items on the quality of the resulting tags.

An ontology-based approach to auto-tagging articles

This paper proposes an auto-tagging methodology using tags defined in the ontology. The auto-tagging methodology consists of two main processes: classification process and tag selection process. The classification process concerns semantic analysis which includes the term-weight matrix and cosine similarity. The tag selection process focuses on the selection of appropriate ontological tag--tag defined in the ontology, for the article. The ontology weight computing is proposed for tag suggestion. A technique for ontology building using blog articles is also presented as well as an extensive experiment. The experiment results show that the proposed approach is an alternative methodology for auto-tagging articles in which the obtained tag is not just the piece of text but it presents the meaning of the articles.

Estimating translation probabilities for social tag suggestion

The task of social tag suggestion is to recommend tags automatically for a user when he or she wants to annotate an online resource. In this study, we focus on how to make use of the text description of a resource to suggest tags. It is intuitive to select significant words from the text description of a source as the suggested tags. However, since users can arbitrarily annotate any tags to a resource, tag suggestion suffers from the vocabulary gap issue — the appropriate tags of a resource may be statistically insignificant or even do not appear in the corresponding description. In order to solve the vocabulary gap issue, in this paper we present a new perspective on social tag suggestion. By considering both a description and tags as summaries of a given resource composed in two languages, tag suggestion can be regarded as a translation from description to tags. We propose two methods to estimate the translation probabilities between words in descriptions and tags. Based on the translation probabilities between words and tags estimated for a large collection of description-tags pairs, we can suggest tags according to the words in a resource description. Experiments on real-world datasets indicate that our methods outperform other methods in precision, recall and F-measure. Moreover, our methods are relatively simple and efficient, which makes them practical for Web applications.

Topic ontology-based efficient tag recommendation approach for blogs

Efficient tag recommendation systems are required to help users in the task of searching, indexing and browsing appropriate blog content. Tag generation has become more popular to annotate web content, other blogs, photos, videos and music. Tag recommendation is an action of signifying valuable and informative tags to a budding item based on the content. We propose a novel approach based on topic ontology for tag recommendation. The proposed approach intelligently generates tag suggestions to blogs. In this paper, we effectively construct the technology entitled Ontology based on Wikipedia categories and WordNet semantic relationship to make the ontology more meaningful and reliable. Spreading activation algorithm is applied to assign interest scores to existing blog content and tags. High quality tags are suggested based on the significance of the interest score. Evaluation proves that the applicability of topic ontology with spreading activation algorithm helps tag recommendation more effective when compared to collaborative tag recommendations. Our proposed approach offers several solutions to tag spamming, sentiment analysis and popularity. Finally, we report the results of an experiment which improves the performance of tag recommendation approach.

A Review of Ontology-Based Tag Recommendation Approaches

Tag recommender schemes suggest related tags for an untagged resource and better tag suggestions to tagged resources. Tagging is very important if the user identifies the tag that is more precise to use in searching interesting blogs. There is no clear information regarding the meaning of each tag in a tagging process. An user can use various tags for the same content, and he can also use new tags for an item in a blog. When the user selects tags, the resultant metadata may comprise homonyms and synonyms. This may cause an improper relationship among items and ineffective searches for topic information. The collaborative tag recommendation allows a set of freely selected text keywords as tags assigned by users. These tags are imprecise, irrelevant, and misleading because there is no control over the tag assignment. It does not follow any formal guidelines to assist tag generation, and tags are assigned to resources based on the knowledge of the users. This causes misspelled tags, multiple tags with the same meaning, bad word encoding, and personalized words without common meaning. This problem leads to miscategorization of items, irrelevant search results, wrong prediction, and their recommendations. Tag relevancy can be judged only by a specific user. These aspects could provide new challenges and opportunities to its tag recommendation problem. This paper reviews the challenges to meet the tag recommendation problem. A brief comparison between existing works is presented, which we can identify and point out the novel research directions. The overall performance of our ontology-based recommender systems is favorably compared to other systems in the literature.

Context-aware mobile image annotation for media search and sharing

In recent years, rapid advances in media technology including acquisition, processing and distribution have led to proliferation of many mobile applications. Amongst them, one of the emerging applications is mobile-based image annotation that uses camera phones to capture images with system-suggested tags before uploading them to the media sharing portals. This procedure can offer information to mobile users and also facilitate the retrieval and sharing of the image for Web users. However, context information that can be acquired from mobile devices is underutilized in many existing mobile image annotation systems. In this paper, we propose a new mobile image annotation system that utilizes content analysis, context analysis and their integration to annotate images acquired from mobile devices. Specifically, three types of context, location, user interaction and Web, are considered in the tagging processes. An image dataset of Nanyang Technological University (NTU) campus has been constructed, and a prototype mobile image tag suggestion system has been developed. The experimental results show that the proposed system performs well in both effectiveness and efficiency on NTU dataset, and shows good potential in domain-specific mobile image annotation for image sharing.

An experimental analysis of suggestions in collaborative tagging

Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource, such as web-page. In this paper we investigate the influence of tag suggestions on the emergence of power-law distributions as a result of collaborative tag behavior. Although previous research has already shown that power-laws emerge in tagging systems, the cause of why power-law distributions emerge is not understood empirically. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power-laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. This imitation behavior leads to a feedback loop in which some tags are reinforced, which is also known as the 'rich get richer' or a preferential attachment model. We present experimental results showing that the power-law distribution forms when tag suggestions are not presented to the users, and the power-law distribution does not hold when tag suggestions are presented to the user. Furthermore, we show that the real effect of tag suggestions is rather subtle; the power-law distribution that would naturally occur without tag suggestions is 'compressed' if tag suggestions are given to the user, resulting in a shorter long tail and a 'compressed' top of the power-law distribution. We show that tag suggestions by themselves do not account for the formation of power-law distributions in tagging systems.

Effective Tag Recommendation System Based on Topic Ontology UsingWikipediaand WordNet

In this paper, we proposed a novel approach based on topic ontology for tag recommendation. The proposed approach intelligently generates tag suggestions to blogs. In this approach, we construct topic ontology through enriching the set of categories in existing small ontology called as Open Directory Project. To construct topic ontology, a set of topics and their associated semantic relationships is identified automatically from the corpus-based external knowledge resources such as Wikipedia and WordNet. The construction relies on two folds such as concept acquisition and semantic relation extraction. In the first fold, a topic-mapping algorithm is developed to acquire the concepts from the semantic of Wikipedia. A semantic similarity-clustering algorithm is used to compute the semantic similarity measure to group the set of similar concepts. The second is the semantic relation extraction algorithm, which derives associated semantic relations between the set of extracted topics from the lexical patterns between synsets in WordNet. A suitable software prototype is created to implement the topic ontology construction process. A Jena API framework is used to organize the set of extracted semantic concepts and their corresponding relationship in the form of knowledgeable representation of Web ontology language. Thus, Protege tool provides the platform to visualize the automatically constructed topic ontology successfully. Using the constructed topic ontology, we can generate and suggest the most suitable tags for the new resource to users. The applicability of topic ontology with a spreading activation algorithm supports efficient recommendation in practice that can recommend the most popular tags for a specific resource. The spreading activation algorithm can assign the interest scores to the existing extracted blog content and tags. The weight of the tags is computed based on the activation score determined from the similarity between the topics in constructed topic ontology and content of the existing blogs. High-quality tags that has the highest activation score is recommended to the users. Finally, we conducted experimental evaluation of our tag recommendation approach using a large set of real-world data sets. Our experimental results explore and compare the capabilities of our proposed topic ontology with the spreading activation tag recommendation approach with respect to the existing AutoTag mechanism. And also discuss about the improvement in precision and recall of recommended tags on the data sets of Delicious and BibSonomy. The experiment shows that tag recommendation using topic ontology results in the folksonomy enrichment. Thus, we report the results of an experiment mean to improve the performance of the tag recommendation approach and its quality. © 2012 Wiley Periodicals, Inc.

Tag recommendation by machine learning with textual and social features

Tags are very popular in social media (like Youtube, Flickr) and provide valuable and crucial information for social media. But at the same time, there exist a great number of noisy tags, which lead to many studies on tag suggestion and recommendation for items including websites, photos, books, movies, and so on. The textual features of tags, likes tag frequency, have mostly been used in extracting tags that are related to items. In this paper, we address the problem of tag recommendation for social media users. This issue is as important as the tag recommendation for items, because the tags representing users are strongly related to the users' favorite topics. We propose several novel features of tags for machine learning that we call social features as well as textual features. The experimental results of Flickr show that our proposed scheme achieves viable performance on tag recommendation for users.