Hashtagger+: Efficient High-Coverage Social Tagging of Streaming News

Shi, Bichen; Poghosyan, Gevorg; Ifrim, Georgiana; Hurley, Neil

doi:10.1109/tkde.2017.2754253

Cited by 26 publications

(25 citation statements)

References 28 publications

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“…In the social media domain, a benchmarking of Big Data architecture using public cloud platforms has been discussed in Persico et al ( 2018 ) for processing social streams. The learning-to-rank framework Hashtagger+ (Shi et al 2018 ) has been developed to analyze social streams in real-time by recommending hashtags to news articles. Furthermore, Wu et al ( 2018 ) developed StreamExplorer based on sliding windows for visually exploring event in these streams.…”

Section: Analyzing Sn Variabilitymentioning

confidence: 99%

A survey of Big Data dimensions vs Social Networks analysis

2020

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The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated data (text, video, image and audio), data heterogeneity and high speed generation rate. More in detail, the analysis of user generated data by popular social networks (i.e Facebook (https://www.facebook.com/), Twitter (https://www.twitter.com/), Instagram (https://www.instagram.com/), LinkedIn (https://www.linkedin.com/)) poses quite intriguing challenges for both research and industry communities in the task of analyzing user behavior, user interactions, link evolution, opinion spreading and several other important aspects. This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V’s).

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Section: Analyzing Sn Variabilitymentioning

confidence: 99%

A survey of Big Data dimensions vs Social Networks analysis

2020

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“…Given that most of the machine/deep learning algorithms have a set of hyperparameters, and that tweaking them will lead to (greatly) different results, a model fine-tuning approach is required for obtaining optimal prediction results. Previous related studies did not report on the details of model fine-tuning for deep learning classifiers, but only report certain combinations of hyperparameters that are manually specified (Shi, Poghosyan, Ifrim, & Hurley, 2017;Tsai et al, 2016;Zhou & Huang, 2017). To perform model fine-tuning in a more exhaustive and efficient manner, we develop a grid search approach with 10-fold cross validation on sequential data, called SeqGridSearch.…”

Section: Fls Classifier Training and Tuningmentioning

confidence: 99%

Analysing forward-looking statements in initial public offering prospectuses: a text analytics approach

Tao

Deokar

Deshmukh

2018

Journal of Business Analytics

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Forward-looking statements (FLSs) have informational value in applications such as predicting stock prices. Management Discussion & Analysis (MD&A) sections in initial public offering (IPO) prospectuses contain FLSs that provide prospective information about the company's future growth and performance. This study focuses on evaluating the relationship between features extracted from FLSs and IPO valuation. To that end, we propose an analytical pipeline for identifying FLSs using machine learning techniques. The FLS classifier is built on the best performing deep learning architecture that outperforms extant methods reported in related studies. In order to demonstrate the value of identified FLSs, we conduct predictive analysis of pre-IPO price revisions and post-IPO first-day returns. We engineer a variety of linguistics features from FLSs including topics, sentiments, readability, semantic similarity, and general text features. The study finds that FLS features are more predictive for pre-IPO as compared to post-IPO valuation prediction. The analytical pipeline contributes to the text classification knowledge base while the findings from the predictive analysis shed light on understanding the underpricing phenomenon occurring in the IPO process.

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“…Many works have been proposed for solving hashtag recommendation problem [5], [6], [30]- [32]. Zhao et al [33] presented the Hashtag-LDA algorithm, a personalized hashtag recommendation approach, that combines a user profiling and lattent dirichlet allocation (LDA) [34].…”

Section: B Hashtag Recommendationmentioning

confidence: 99%

A Data-Driven Approach for Twitter Hashtag Recommendation

et al. 2020

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This paper addresses the hashtag recommendation problem using high average-utility pattern mining. We introduce a novel framework called PM-HRec (Pattern Mining for Hashtag Recommendation). It consists of two main stages. First, offline processing transforms the corpus of tweets into a transactional database considering the temporal information of the tagged tweets (tweets with hashtags). The method discovers the temporal top k high average utility patterns. Irrelevant tagged tweets and the ontology of tagged tweets are also constructed offline. Second, an online processing inputs the utility patterns, the ontology, and the irrelevant tagged tweets to extract the most relevant hashtags for a given orpheline tweet (tweet without hashtags). Extensive experiments were carried out on large tweets collections. The proposed PM-HRec outperforms the existing state of the art hashtag recommendation approaches in terms of quality of recommended hashtags and runtime processing. INDEX TERMS High average utility patterns, hashtag recommendation, ontology construction, temporal information.

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Hashtagger+: Efficient High-Coverage Social Tagging of Streaming News

Cited by 26 publications

References 28 publications

A survey of Big Data dimensions vs Social Networks analysis

A survey of Big Data dimensions vs Social Networks analysis

Analysing forward-looking statements in initial public offering prospectuses: a text analytics approach

A Data-Driven Approach for Twitter Hashtag Recommendation

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