Modeling individual topic-specific behavior and influence backbone networks in social media

Bogdanov, Petko; Busch, Michael; Moehlis, Jeff; Singh, Ambuj K.; Szymański, Bolesław K.

doi:10.1007/s13278-014-0204-6

Cited by 18 publications

(20 citation statements)

References 26 publications

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“…Error rate 40,41 determines that the predicted output values are inaccurate, ie, it is defined as a proportion of total number of bad predictions among all total inspected cases. Error rate 40,41 determines that the predicted output values are inaccurate, ie, it is defined as a proportion of total number of bad predictions among all total inspected cases.…”

Section: Performance Measuresmentioning

confidence: 99%

“…The novel findings were the time lags between retweets, the co-occurrence of URLS and hashtags, and the sentiment expressed in the tweet. A genetically inspired framework was proposed by Bogdanov et al40 for modeling individual social media users which they termed a genotype. They build prediction models based on a variety of behavioral and contextual features using NB, LR, DT techniques.…”

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Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Kumar

Jaiswal

2019

Concurrency and Computation

130

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Sentiment detection and classification is the latest fad for social analytics on Web. With the array of practical applications in healthcare, finance, media, consumer markets, and government, distilling the voice of public to gain insight to target information and reviews is non-trivial. With a marked increase in the size, subjectivity, and diversity of social web-data, the vagueness, uncertainty and imprecision within the information has increased manifold. Soft computing techniques have been used to handle this fuzziness in practical applications. This work is a study to understand the feasibility, scope and relevance of this alliance of using Soft computing techniques for sentiment analysis on Twitter. We present a systematic literature review to collate, explore, understand and analyze the efforts and trends in a well-structured manner to identify research gaps defining the future prospects of this coupling. The contribution of this paper is significant because firstly the primary focus is to study and evaluate the use of soft computing techniques for sentiment analysis on Twitter and secondly as compared to the previous reviews we adopt a systematic approach to identify, gather empirical evidence, interpret results, critically analyze, and integrate the findings of all relevant high-quality studies to address specific research questions pertaining to the defined research domain. KEYWORDS machine learning, review, sentiment analysis, soft computing, Twitter INTRODUCTIONThe incessantly evolving dynamics of the Web in terms of the volume, velocity and variety of opinion-rich information accessible online, has made research in the domain of Sentiment Analysis (SA) a trend for many practical applications which facilitate decision support and deliver targeted information to domain analysts. Interestingly, the buzzing term ''big data'' which is estimated to be 90% unstructured 1 further makes it crucial to tap and analyze information using contemporary tools. Text mining models define the process to transform and substitute this unstructured data into a structured one for knowledge discovery. Use of classification algorithms to intelligently mine text has been studied extensively across literature. 2,3 SA, established as a typical text classification task, 4 is defined as the computational study of people's opinions, attitudes and emotions towards an entity. 5,6 It offers a technology-based solution to understand people's reactions, views and opinion polarities (positive, negative or neutral) in textual content available over social media sources.Research studies and practical applications in the field of SA have escalated in the past decade with the transformation and expansion of Web from passive provider of content to an active socially-aware distributor of collective intelligence. This new collaborative Web (called Web 2.0), 7 extended by Web-based technologies like comments, blogs and wikis, social media portals like Twitter or Facebook, that allow to build social networks based on professional relationship, i...

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Section: Performance Measuresmentioning

confidence: 99%

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confidence: 99%

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Kumar

Jaiswal

2019

Concurrency and Computation

130

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“…Kianian et al [13] proposed a semantic community discovery algorithm based on a label propagation algorithm. Based on the higher degree of intimacy between user nodes in social networks, the topic distribution is more similar [14,15]. Hu et al proposed the FT (feature topic) model for user semantic information analysis and the close relationship between users.…”

Section: Related Workmentioning

confidence: 99%

Tibetan Weibo User Group Division Based on Semantic Information in the Era of Big Data

Qiu

et al. 2018

Complexity

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In the era of big data, group division in online social network analysis is a basic task. It can be divided into the group division based on static relationship and the group division based on dynamic relationship. Compared with the static group division, users express their semantic information in all kinds of social network behaviors, and they tend to interact with other users who have the same idea and attitude; this is how different groups are formed. In this paper, aimed at the issue that some Tibetan users use Chinese to publish microblogs on social platforms, a group division method based on semantic information of Tibetan users under the big data environment is proposed. When dividing a large number of Tibetan user groups in a social network, a large amount of semantic information of Tibetan users in the social network is first analyzed. Then, based on the semantic similarity between users, we aggregate the Tibetan users with high similarities into one group, thus achieving the final group division. The experimental results illustrate the effectiveness of the method of analyzing Tibetan user semantic information in the context of big data for group partitioning.

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“…Recommender systems have become increasingly indispensable in many applications including movie recommendation [16], hashtag recommendation [2], music recommendation [5], news recomPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored.…”

Section: Introductionmentioning

confidence: 99%

Dual-Regularized One-Class Collaborative Filtering

Yao

Tong

Yan

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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Collaborative filtering is a fundamental building block in many recommender systems. While most of the existing collaborative filtering methods focus on explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class setting where user feedback is implicitly expressed (e.g., views in news recommendation and video recommendation). The main challenges in such one-class setting include the ambiguity of the unobserved examples and the sparseness of existing positive examples.In this paper, we propose a dual-regularized model for one-class collaborative filtering. In particular, we address the ambiguity challenge by integrating two state-of-the-art one-class collaborative filtering methods to enjoy the best of both worlds. We tackle the sparseness challenge by exploiting the side information from both users and items. Moreover, we propose efficient algorithms to solve the proposed model. Extensive experimental evaluations on two real data sets demonstrate that our method achieves significant improvement over the state-of-the-art methods. Overall, the proposed method leads to 7.9% -21.1% improvement over its best known competitors in terms of prediction accuracy, while enjoying the linear scalability.

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Modeling individual topic-specific behavior and influence backbone networks in social media

Cited by 18 publications

References 26 publications

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Tibetan Weibo User Group Division Based on Semantic Information in the Era of Big Data

Dual-Regularized One-Class Collaborative Filtering

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