Design and Implementation of a Toolkit for the Aspect-Based Sentiment Analysis of Tweets

Hagge, Marvin; Hoffen, Moritz von; Betzing, Jan Hendrik; Becker, Jörg

doi:10.1109/cbi.2017.68

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Hagge Marvin et al [8] described that the micro-blogging service identi ed consumer sentiment i.e. This is Facebook.…”

Section: Review Of Classi Cation Techniquesmentioning

confidence: 99%

A novel Security Aware Sensitive Encrypted Storage approach to improve the encryption of big data

Gupta¹,

Lakhwani

2020

Preprint

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The data security and privacy have become a critical issue that restricts many cloud applications. One of the major concerns about security and privacy is the fact that cloud operators have the opportunity to access sensitive data. This concern dramatically increases user anxieties and reduces the acceptability of cloud computing in many areas, such as the financial industry and government agencies. This paper focuses on this issue and proposes an intelligent approach to cryptography, which would make it impossible for cloud service operators to reach sensitive data directly. The suggested method divides the file with precision using an intelligent classification technique. An alternative approach is designed to determine whether data packets need splitting to shorten operating time and reduce storage space. Our experimental assessments of both safety and efficiency performance and experimental results show that our approach can effectively address major cloud hazards and that it requires an acceptable computing time using an intelligent machine learning classification technique. We have proposed a novel approach entitled as a model for Security Aware Sensitive Encrypted Storage (SA-SES). In this model, we used our proposed algorithms, including Convolution Neural Network with Logistic Regression (CNN-LR), Elliptic-curve Diffie–Hellman-Shifted Adaption Homomorphism Encryption (ECDH-SAHE) and Elliptic-curve Diffie–Hellman-Shifted Adaption Homomorphism Decryption (ECDH-SAHD) .

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“…Hagge Marvin et al [8] described that the micro-blogging service identi ed consumer sentiment i.e. This is Facebook.…”

Section: Review Of Classi Cation Techniquesmentioning

confidence: 99%

A novel Security Aware Sensitive Encrypted Storage approach to improve the encryption of big data

Gupta¹,

Lakhwani

2020

Preprint

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“…In particular, the Twitter aspect-based sentiment classification process in [81] consists of the following main steps: aspect-sentiment extraction, aspect ranking and selection, and aspect classification, whereas Lau et al [84] use NER to parse product names to determine their polarity. The aspect-based sentiment analysis approach in [59] leveraged POS tagging and dependency parsing. Moreover, [497] proposed a hybrid approach to analyse aspect-based sentiment of tweets.…”

Section: Aspect-based Social Opinion Miningmentioning

confidence: 99%

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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An Integrated Non-linear Deep Learning Method for Sentiment Classification of Online Reviews

Singh

Wibowo

Grandhi

2021

Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery

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Design and Implementation of a Toolkit for the Aspect-Based Sentiment Analysis of Tweets

Cited by 6 publications

References 27 publications

A novel Security Aware Sensitive Encrypted Storage approach to improve the encryption of big data

A novel Security Aware Sensitive Encrypted Storage approach to improve the encryption of big data

Over a Decade of Social Opinion Mining: A Systematic Review

An Integrated Non-linear Deep Learning Method for Sentiment Classification of Online Reviews

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