Transformer based Deep Intelligent Contextual Embedding for Twitter sentiment analysis

Naseem, Usman; Razzak, Imran; Musiał, Katarzyna; Imran, Muhammad

doi:10.1016/j.future.2020.06.050

Cited by 196 publications

(99 citation statements)

References 21 publications

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“…To address the limitation of fixed word embeddings, the contextualized word representations (e.g., ELMo) were proposed by Peters et al [13] and have been extensively used in recent works [14]− [16]. Following this elegant recipe, we employ contextualized word representations to better represent out-of-vocabulary words as well as capture contextaware word semantics.…”

Section: A Research Backgroundmentioning

confidence: 99%

Diversified Semantic Attention Model for Fine-Grained Entity Typing

Xue

Luo

et al. 2021

IEEE Access

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Section: A Research Backgroundmentioning

confidence: 99%

Diversified Semantic Attention Model for Fine-Grained Entity Typing

Xue

Luo

et al. 2021

IEEE Access

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“…Social network (SN) sites are a dynamic platform that is now being utilized for different purposes such as education, business, medical purposes, telemarketing, but also, unfortunately, unlawful activities (Vartapetiance and Gillam 2014;Wu et al 2019;Naseem et al 2020). Generally, people use SN to socialize with their interested friends and colleagues.…”

Section: Introductionmentioning

confidence: 99%

“…2019 ; Naseem et al. 2020 ). Generally, people use SN to socialize with their interested friends and colleagues.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

188

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Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

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“…More specifically, the essence of sentiment analysis consists in extracting an aspect term of an input sentence to determine its polarity as positive, neutral and negative as authors remark in [8] and it is generally solved as a multiclass classification problem. Moreover, sentiment analysis has widely been used in numerous studies for both reviews and user opinions analysis in online commercial platforms [16], [47] and user behaviour mining in social media platforms such as Twitter [6], [29], [37], [42].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers

et al. 2021

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During the last decades, the volume of multimedia content posted in social networks has grown exponentially and such information is immediately propagated and consumed by a significant number of users. In this scenario, the disruption of fake news providers and bot accounts for spreading propaganda information as well as sensitive content throughout the network has fostered applied research to automatically measure the reliability of social networks accounts via Artificial Intelligence (AI). In this paper, we present a multilingual approach for addressing the bot identification task in Twitter via Deep learning (DL) approaches to support end-users when checking the credibility of a certain Twitter account. To do so, several experiments were conducted using state-of-the-art Multilingual Language Models to generate an encoding of the text-based features of the user account that are later on concatenated with the rest of the metadata to build a potential input vector on top of a Dense Network denoted as Bot-DenseNet. Consequently, this paper assesses the language constraint from previous studies where the encoding of the user account only considered either the metadata information or the metadata information together with some basic semantic text features. Moreover, the Bot-DenseNet produces a low-dimensional representation of the user account which can be used for any application within the Information Retrieval (IR) framework.

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Transformer based Deep Intelligent Contextual Embedding for Twitter sentiment analysis

Cited by 196 publications

References 21 publications

Diversified Semantic Attention Model for Fine-Grained Entity Typing

Diversified Semantic Attention Model for Fine-Grained Entity Typing

Deep learning for misinformation detection on online social networks: a survey and new perspectives

A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers

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