Comparison of Traditional Machine Learning and Deep Learning Approaches for Sentiment Analysis

Kansara, Dhvani; Sawant, Vinaya

doi:10.1007/978-981-15-3242-9_35

Cited by 11 publications

(6 citation statements)

References 3 publications

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“…Recurrent Neural Networks with Long Short-Term Memory and Convolutional Neural Networks, etc.) to produce more accurate results than classic ML techniques, but they require more effort for algorithms training [9]. Lexicon-based and ML-Based methods are not mutually exclusive and could be coupled together to extrapolate different information from the same data-set.…”

mentioning

confidence: 99%

Case Studies on the Use of Sentiment Analysis to Assess the Effectiveness and Safety of Health Technologies: A Scoping Review

Polisena¹,

Andellini

Salerno

et al. 2021

IEEE Access

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A health technology assessment (HTA) is commonly defined as a multidisciplinary approach used to evaluate medical, social, economic, and ethical issues related to the use of a health technology in a systematic, transparent, unbiased, robust manner. To help inform HTA recommendations, the surveillance of social media platforms can provide important insights to the clinical community and to decision makers on the effectiveness and safety of the use of health technologies on a patient. A scoping review of the published literature was performed to gain some insight on the accuracy and automation of sentiment analysis (SA) used to assess public opinion on the use of health technologies. A literature search of major databases was conducted. The main search concepts were SA, social media, and patient perspective. Among the 1,776 unique citations identified, 12 studies that described the use of SA methods to evaluate public opinion on or experiences with the use of health technologies as posted on social media platforms were included. The SA methods used were either lexicon-or machine learning-based. Two studies focused on medical devices, three examined HPV vaccination, and the remaining studies targeted drug therapies. Due to the limitations and inherent differences among SA tools, the outcomes of these applications should be considered exploratory. The results of our study can initiate discussions on how the automation of algorithms to interpret public opinion of health technologies should be further developed to optimize the use of data available on social media.

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mentioning

confidence: 99%

Case Studies on the Use of Sentiment Analysis to Assess the Effectiveness and Safety of Health Technologies: A Scoping Review

Polisena¹,

Andellini

Salerno

et al. 2021

IEEE Access

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show abstract

“…Datta and Gupta (2023) proposed the use of multiple hidden layers in the CNN model for the accurate identification of disease classes. The deep learning models performed exceedingly well mainly because the hidden layers have a profound understanding of the immense amount of data (Kansara and Sawant, 2020). However, the accuracy of the model can differ with the number of hidden layers in the neural network.…”

Section: Training Of Modelsmentioning

confidence: 99%

Tea Leaf Disease Classification Using Artificial Intelligence (AI) Models

Kumaratenna,

Cho

2024

J. Bio-Env. Con.

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In this study, five artificial intelligence (AI) models: Inception v3, SqueezeNet (local), VGG-16, Painters, and DeepLoc were used to classify tea leaf diseases. Eight image categories were used: healthy, algal leaf spot, anthracnose, bird's eye spot, brown blight, gray blight, red leaf spot, and white spot. Software used in this study was Orange 3 which functions as a Python library for visual programming, that operates through an interface that generates workflows to visually manipulate and analyze the data. The precision of each AI model was recorded to select the ideal AI model. All models were trained using the Adam solver, rectified linear unit activation function, 100 neurons in the hidden layers, 200 maximum number of iterations in the neural network, and 0.0001 regularizations. To extend the functionality of Orange 3, new add-ons can be installed and, this study image analytics add-on was newly added which is required for image analysis. For the training model, the import image, image embedding, neural network, test and score, and confusion matrix widgets were used, whereas the import images, image embedding, predictions, and image viewer widgets were used for the prediction. Precisions of the neural networks of the five AI models (Inception v3, SqueezeNet (local), VGG-16, Painters, and DeepLoc) were 0.807, 0.901, 0.780, 0.800, and 0.771, respectively. Finally, the SqueezeNet (local) model was selected as the optimal AI model for the detection of tea diseases using tea leaf images owing to its high precision and good performance throughout the confusion matrix.

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“…Another challenging task for a model is to extract context from various domain specific language. Empirical studies have shown that rule-based methods and traditional machine learning-based methods fail to overcome these complexities by understanding the inherent meaning of the sentences (Kansara and Sawant, 2020;González-Carvajal and Garrido-Merchán, 2020). Multilingualism is another challenge with classic machine learning techniques (González-Carvajal and Garrido-Merchán, 2020).…”

Section: Baseline Models Selectionmentioning

confidence: 99%