Aspect-based sentiment analysis using adaptive aspect-based lexicons

Mowlaei, Mohammad Erfan; Abadeh, Mohammad Saniee; Keshavarz, Hamidreza

doi:10.1016/j.eswa.2020.113234

Cited by 125 publications

(70 citation statements)

References 22 publications

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“…The generated lexicon is fused with static lexicons such as SentiWordNet and the combined lexicon is employed in aspect-based sentiment analysis. Their experiments showed good performance of the proposed method [11].…”

Section: Introductionmentioning

confidence: 91%

The Effect of Pre-processing Techniques on the Accuracy of Sentiment Analysis Using Bag-of-Concepts Text Representation

Mehanna

Mahmuddin

2021

SN COMPUT. SCI.

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Section: Introductionmentioning

confidence: 91%

The Effect of Pre-processing Techniques on the Accuracy of Sentiment Analysis Using Bag-of-Concepts Text Representation

Mehanna

Mahmuddin

2021

SN COMPUT. SCI.

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“…The term relates to a single word, phrase, or expression (Chiavetta, Bosco & Pilato, 2016). Mowlaei, Abadeh & Keshavarz (2020) addressed a problem of reduced performance of aspect-based methods due to the failure to adapt general lexicons of datasets based on aspects. To address this issue, an extension is proposed of two lexicon-generation procedures.…”

Section: Lexicon Based Approachesmentioning

confidence: 99%

Multi-level aspect based sentiment classification of Twitter data: using hybrid approach in deep learning

Janjua¹,

Siddiqui²,

Sindhu³

et al. 2021

PeerJ Computer Science

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Social media is a vital source to produce textual data, further utilized in various research fields. It has been considered an essential foundation for organizations to get valuable data to assess the users’ thoughts and opinions on a specific topic. Text classification is a procedure to assign tags to predefined classes automatically based on their contents. The aspect-based sentiment analysis to classify the text is challenging. Every work related to sentiment analysis approached this issue as the current research usually discusses the document-level and overall sentence-level analysis rather than the particularities of the sentiments. This research aims to use Twitter data to perform a finer-grained sentiment analysis at aspect-level by considering explicit and implicit aspects. This study proposes a new Multi-level Hybrid Aspect-Based Sentiment Classification (MuLeHyABSC) approach by embedding a feature ranking process with an amendment of feature selection method for Twitter and sentiment classification comprising of Artificial Neural Network; Multi-Layer Perceptron (MLP) is used to attain improved results. In this study, different machine learning classification methods were also implemented, including Random Forest (RF), Support Vector Classifier (SVC), and seven more classifiers to compare with the proposed classification method. The implementation of the proposed hybrid method has shown better performance and the efficiency of the proposed system was validated on multiple Twitter datasets to manifest different domains. We achieved better results for all Twitter datasets used for the validation purpose of the proposed method with an accuracy of 78.99%, 84.09%, 80.38%, 82.37%, and 84.72%, respectively, compared to the baseline approaches. The proposed approach revealed that the new hybrid aspect-based text classification functionality is enhanced, and it outperformed the existing baseline methods for sentiment classification.

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“…Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence DOI: http://dx.doi.org /10.5772/intechopen.93488 expected that as this shift continues, new opportunities for ML and AI applications will become available, including in modeling consumer behavior and facilitating sustainable behavior change energy consumption action [3, 65,118,119], estimating and predicting the marginal emissions of residential energy utilization and thermal comfort in buildings in real time, on a scale of hours [57,118], and game-theoretic modeling and design of socially beneficial energy policies like social norms, public opinions, stakeholder engagement, and education efforts [120][121][122]. Other breakthrough innovations might displace fossil fuels leading to stranding, and creating opportunities for ML-based electricity pricing techniques and rate design to set dynamic pricing of carbon, electricity, and consumer choice [1,[123][124][125][126][127], and multiobjective optimization to compute Pareto-optimal solutions for climate engineering, climate informatics, and solar geoengineering [58,[128][129][130]. There is a possibility that these technological innovations could create a sudden improvement in market evaluation of the renewable energy industries, while some assets of related carbonintensive industries become stranded due to obsolescence, write-offs, or retirements.…”

Section: Nuclear Fission and Fusion: Applicationmentioning

confidence: 99%

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Nyangon¹

2021

Sustainable Energy Investment - Technical, Market and Policy Innovations to Address Risk

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The Paris Agreement on climate change requires nations to keep the global temperature within the 2°C carbon budget. Achieving this temperature target means stranding more than 80% of all proven fossil energy reserves as well as resulting in investments in such resources becoming stranded assets. At the implementation level, governments are experiencing technical, economic, and legal challenges in transitioning their economies to meet the 2°C temperature commitment through the nationally determined contributions (NDCs), let alone striving for the 1.5°C carbon budget, which translates into greenhouse gas emissions (GHG) gap. This chapter focuses on tackling the risks of stranded electricity assets using machine learning and artificial intelligence technologies. Stranded assets are not new in the energy sector; the physical impacts of climate change and the transition to a low-carbon economy have generally rendered redundant or obsolete electricity generation and storage assets. Low-carbon electricity systems, which come in variable and controllable forms, are essential to mitigating climate change. These systems present distinct opportunities for machine learning and artificial intelligence-powered techniques. This chapter considers the background to these issues. It discusses the asset stranding discourse and its implications to the energy sector and related infrastructure. The chapter concludes by outlining an interdisciplinary research agenda for mitigating the risks of stranded assets in electricity investments.

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Aspect-based sentiment analysis using adaptive aspect-based lexicons

Cited by 125 publications

References 22 publications

The Effect of Pre-processing Techniques on the Accuracy of Sentiment Analysis Using Bag-of-Concepts Text Representation

The Effect of Pre-processing Techniques on the Accuracy of Sentiment Analysis Using Bag-of-Concepts Text Representation

Multi-level aspect based sentiment classification of Twitter data: using hybrid approach in deep learning

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

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