Targeted Aspect-Based Sentiment Analysis for Ugandan Telecom Reviews from Twitter

Kabiito, David; Nakatumba‐Nabende, Joyce

doi:10.1007/978-3-030-70296-0_24

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Tables 1 and 2 show the accuracy of the proposed Fusion Attention algorithm and its assessment with the existing methods like Res-Target (10) , VGG16 (15) , Resnet50 (17) and Alexnet (20) . Considering the existing VGG-16 networks, it possesses 13 convolutional layers as well as 3 fully connected layers.…”

Section: Resultsmentioning

confidence: 99%

“…Driven by the consideration that, a sentiment typically includes undeniable levels of reflection, and this might be simpler as classification of images. Only few papers have concentrated in utilizing the visual elements or characteristics for examination of visual sentiments (7)(8)(9)(10) . The significant disadvantage of these methodologies is that, training requires lot of spatial information on psychological research or phonetics to characterize the mid-level characteristics as well as human mediation for calibrating the expectation results.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Fusion Attention Algorithm for Sentimental Image Analysis

Roseline¹,

Chellam²

2022

IJST

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Objectives: To implement a novel and hybrid methodology for finding out the positive features when using convolutional neural networks (CNNs) for visual sentiment analysis. To achieve increased accuracy, precision and recall by using this proposed fusion attention methodology. Methods: This study proposes a modified methodology encompassing spatial attention, channel attention as well as squeeze excitation modules. An enhanced approach on the basis of convolutional neural networks was used here which utilizes convolution operators by combining both spatial and channel-based data. Moreover, we have incorporated three considerations like spatial, channel as well as squeeze and excitation at various levels for attaining optimal results. Findings: The accuracy of the existing approaches was 59.88%, 60.06%, 59.28% and 62.89%, but the proposed fusion attention method showed increased accuracy of 64.15%. Similarly, the F1 score of existing approaches are 0.464804, 0.250164, 0.474129 and 0.2574, but the proposed method revealed increased F1 score of 0.512933. Furthermore, the proposed algorithm showed precision and recall of 0.560896 and 0.472526 which were better when compared with the existing approaches like Res-Target, Resnet50, Alexnet and VGG16. Novelty: The novel feature of this proposed fusion attention algorithm was that it incorporates a hybrid approach in which the image together with convolution passes through channel attention, spatial attention as well as squeeze and excitation so as to attain increased accuracy, but most of the existing approaches have used only channel attention and spatial attention modules. In this proposed method, the algorithm performs convolution in 64-bit, 128-bit and 256-bit respectively together in which the three attentions were interchanged in each convolution, which were not prevalent in the existing approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Fusion Attention Algorithm for Sentimental Image Analysis

Roseline¹,

Chellam²

2022

IJST

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Developing and Deploying End‐to‐End Machine Learning Systems for Social Impact: A Rubric and Practical Artificial Intelligence Case Studies From African Contexts

Bainomugisha,

Nakatumba‐Nabende

2024

Applied AI Letters

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Artificial intelligence (AI) and machine learning have demonstrated the potential to provide solutions to societal challenges, for example, automated crop diagnostics for smallholder farmers, environmental pollution modelling and prediction for cities and machine translation systems for languages that enable information access and communication for segments of the population who are unable to speak or write official languages, among others. Despite the potential of AI, the practical and technical issues related to its development and deployment in the African context are the least documented and understood. The development and deployment of AI for social impact systems in the developing world present new intricacies and requirements emanating from the unique technology and social ecosystems in these settings. This paper provides a rubric for developing and deploying AI systems for social impact with a focus on the African context. The rubric is derived from the analysis of a series of selected real‐world case studies of AI applications in Africa. We assessed the selected AI case studies against the proposed rubric. The rubric and examples of AI applications presented in this paper are expected to contribute to the development and application of AI systems in other African contexts.

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