URWF: user reputation based weightage framework for twitter micropost classification

Bukhari, Asad; Qamar, Usman; Ghazia, Ume

doi:10.1007/s10257-016-0320-0

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…In light of the above, it is noteworthy that no systematic review within this newly defined domain exists even though there are several good survey papers [4,8,19,2]. The research paper by Bukhari et al [20] is closest to a systematic review in this domain, whereby the authors performed a search over the ScienceDirect and SpringerLink electronic libraries for the "sentiment analysis", "sentiment analysis models", "sentiment analysis of microblogs" terms. As a result, we felt that the Social Opinion Mining domain well and truly deserves a thorough systematic review that captures all of the relevant research conducted over the last decade.…”

Section: Systematic Reviewmentioning

confidence: 99%

“…the Advancement of Artificial Intelligence (AAAI) 19 , Association for Computational Linguistics (ACL) 20 and Wiley Online Library 21 .…”

Section: Study Selection -Additional Setmentioning

confidence: 99%

“…A total of 9 studies [302,303,58,83,304,20,207,305,306] adopted a statistical approach to perform a form of social opinion mining. In particular, one of the approaches proposed in [58] uses the term frequency-inverse document frequency (tf-idf) numerical statistic to find out the important words within a tweet, to dynamically enrich Twitter specific dictionaries created by the authors.…”

Section: Statistical (St)mentioning

confidence: 99%

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Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

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Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing and other

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Section: Systematic Reviewmentioning

confidence: 99%

“…the Advancement of Artificial Intelligence (AAAI) 19 , Association for Computational Linguistics (ACL) 20 and Wiley Online Library 21 .…”

Section: Study Selection -Additional Setmentioning

confidence: 99%

Section: Statistical (St)mentioning

confidence: 99%

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Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

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A Sentiment Analysis of Twitter Data Using Bi-Directional Long Short Term Memory

Kumar

Rao

2019

Learning and Analytics in Intelligent Systems

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Over a decade of social opinion mining: a systematic review

Cortis

Davis

2021

Artif Intell Rev

View full text Add to dashboard Cite

Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.

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URWF: user reputation based weightage framework for twitter micropost classification

Cited by 7 publications

References 22 publications

Over a Decade of Social Opinion Mining: A Systematic Review

Over a Decade of Social Opinion Mining: A Systematic Review

A Sentiment Analysis of Twitter Data Using Bi-Directional Long Short Term Memory

Over a decade of social opinion mining: a systematic review

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