Performance comparison of text-based sentiment analysis using recurrent neural network and convolutional neural network

Purnamasari, Prima Dewi; Taqiyuddin, Muhammad; Ratna, Anak Agung Putri

doi:10.1145/3162957.3163012

Cited by 4 publications

(2 citation statements)

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“…With advancements in information technology, an increasing number of machine learning models are being employed to analyze geological disaster susceptibility. Many experts have invested in deep learning models such as the Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) for the study of land hazard vulnerability [86,87]. However, there are also some disadvantages, such as the requirement of an extensive number of parameters; unobservable learning processes; and difficulties interpreting the classification results in the models.…”

Section: Discussionmentioning

confidence: 99%

Geological Disaster Susceptibility Evaluation Using Machine Learning: A Case Study of the Atal Tunnel in Tibetan Plateau

Bian,

Chen,

Liu

et al. 2024

Sustainability

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Tunnels serve as vital arteries in the realm of transportation and infrastructure, facilitating the seamless flow of movement across challenging terrains. With the increasing demand experienced by the traffic network on the Tibetan Plateau, deep-buried, lengthy tunnels have become one of the extremely important types of roads for local residents to pass through. Geological disaster susceptibility mapping by hybrid models has been proven to be an effective means to reduce the losses caused by disasters in a large area. However, there has been relatively little research conducted in tunnel areas with significant human activity. To explore the feasibility of conducting geological disaster susceptibility assessment in tunnel areas, we chose the Atal Tunnel as a study project; as a strategic passageway, this exemplifies the significant geological hurdles encountered on the Tibetan Plateau. Employing multi-source remote sensing data, we meticulously mapped the distribution of geological disasters and identified nine environmental and geological variables pivotal for susceptibility evaluation. We harnessed interpretable ensemble learning models to assess this susceptibility, comparing the efficacy of four distinct models: the weight of evidence method (WoE), the frequency ratio (FR), logistic regression (LR) and the support vector machine (SVM). The precision of our findings was rigorously tested using metrics such as the percentage of disaster area encompassed within each risk level, the Area Under the Curve (AUC) value, and the Receiver Operating Characteristic (ROC) curve, and all results were highly accurate. Notably, the WoE-LR model achieved superior performance, boasting an impressive accuracy rate of 90.7%. Through model interpretation, we discerned that the alignment of the road line is the most critical determinant in the evaluation of tunnel geological disaster susceptibility, underscoring the high precision of our model. The extension and successful application of this research in plateau areas hold profound implications for sustainable development. Moreover, the practical application of these research findings will provide a practical reference for the design and construction of projects in similar plateau areas, with positive outcomes that extend well beyond the immediate geographical area of the projects.

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Section: Discussionmentioning

confidence: 99%

Geological Disaster Susceptibility Evaluation Using Machine Learning: A Case Study of the Atal Tunnel in Tibetan Plateau

Bian,

Chen,

Liu

et al. 2024

Sustainability

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“…RNN is a nonlinear model that can learn deeper structured information. Purnamasari et al (2017) showed RNN is better than CNN. However, despite the praises that the model has earned, it is worth noting that RNN suffers from exploding gradient problems while learning long range dependencies.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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Banking is an essential component of our day-to-day activity, and the sector contributes to the development of every Nation's economy. To ensure bank stability, the banks have gone through several deregulation and reformation processes. Unfortunately, these processes and technological advancement have led to increased competitiveness, saturated market, and low profitability. Thus, the need for banks to build customer centric service to gain profitability and stability is vital. This opens the need to investigate customers attitude towards banking.With the growing usage of social network sites (SNS), the user generated content (UGC) has opened opportunities for banks to mine customers opinion . This can help the banks to generate insight into their product and services, create marketing strategies and manage their reputation. To the customers, this serves as source to information that can help in supporting decision making on product or service purchase. In this study, sentiment analysis (SA) techniques were employed to investigate customers attitude towards banking us ing Twitter data. However, the unstructured nature of the data and word ambiguity made sentiment analysis complicated. In the context of this study, SA is more difficult because it involves the natural language processing of Pidgin English and English words in the bank domain.Unfortunately, there are limited or no resources for this purpose.

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On the Sensitivity of LSTMs to Hyperparameters and Word Embeddings in the Context of Sentiment Analysis

El Haddaoui,

Chiheb,

Faizi

et al. 2022

Proceedings of the 5th International Conference on Big Data and Internet of Things

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Performance comparison of text-based sentiment analysis using recurrent neural network and convolutional neural network

Cited by 4 publications

References 4 publications

Geological Disaster Susceptibility Evaluation Using Machine Learning: A Case Study of the Atal Tunnel in Tibetan Plateau

Geological Disaster Susceptibility Evaluation Using Machine Learning: A Case Study of the Atal Tunnel in Tibetan Plateau

Statistical learning approaches to sentiment analysis in the Nigerian banking context

On the Sensitivity of LSTMs to Hyperparameters and Word Embeddings in the Context of Sentiment Analysis

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