Research Output to Industry Use: A Readiness Study for Topic Modelling with Sentiment Analysis

Tan, Jih Soong; Chia, Wai Chong

doi:10.1007/978-981-16-8515-6_2

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…VADER [36], and TextBlob lexicon [37], relying on the semantic SA method that suffers from the issue of neglecting a neutral score. This problem is solved by applying the POS (PENN) tagging techniques like (JJ.…”

Section: Lexicon Generationmentioning

confidence: 99%

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Waheeb¹

2023

Computer Systems Science and Engineering

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Automatic extraction of the patient's health information from the unstructured data concerning the discharge summary remains challenging. Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field. Using a sentiment dictionary and feature engineering, the researchers primarily mine semantic text features. However, choosing and designing features requires a lot of manpower. The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space. A composite model including Active Learning (AL), Convolutional Neural Network (CNN), BiGRU, and Multi-Attention, called ACBMA in this research, is designed to measure the quality of treatment based on discharge summaries text sentiment detection. CNN is utilized for extracting the set of local features of text vectors. Then BiGRU network was utilized to extract the text's global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network (RNN) gradient disappearance. Experiments prove that the ACBMA method can demonstrate the effectiveness of the suggested method, achieve comparable results to state-of-arts methods in sentiment detection, and outperform them with accurate benchmarks. Finally, several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.

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Section: Lexicon Generationmentioning

confidence: 99%

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Waheeb¹

2023

Computer Systems Science and Engineering

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“…The of the most significant challenges in the medical domain. The nov and integrate the lexicon-based sentimental scores have been intr process of beep learning through an attention mechanism to addre was separated into two sections, as follows: (i) This section compares the proposed BOW approach with UMLS [54], VADER [55], and TextBlob lexicon [56], relyi method that suffers from the issue of neglecting a neutral solved by applying the POS (PENN) tagging techniques like retrieved from www.cs.nyu.edu/grishman/jet/guide/PennPO Next, two lists of the terms were generated, wherein BOW i cons are fused as the second list that relied on the hypernym (ii) In the second section, the sentiment-specific word embedding learn the sentimental orientation of features in the existing…”

Section: Lexicon Generationmentioning

confidence: 99%

“…(i) This section compares the proposed BOW approach with SentWordNet [53] and UMLS [54], VADER [55], and TextBlob lexicon [56], relying on the semantic SA method that suffers from the issue of neglecting a neutral score. Next, two lists of the terms were generated, wherein BOW is the first, and four lexicons are fused as the second list that relied on the hypernym's procedure.…”

Section: Lexicon Generationmentioning

confidence: 99%

Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach

Waheeb

2023

Applied Sciences

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This paper discusses the challenges associated with a class imbalance in medical data and the limitations of current approaches, such as machine multi-task learning (MMTL), in addressing these challenges. The proposed solution involves a novel hybrid data sampling method that combines SMOTE, a meta-weigher with a meta-based self-training method (MMS), and one-sided selection (OSS) to balance the distribution of classes. The method also utilizes condensed nearest neighbors (CNN) to remove noisy majority examples and redundant examples. The proposed technique is twofold, involving the creation of artificial instances using SMOTE-OSS-CNN to oversample the under-represented class distribution and the use of MMS to train an instructor model that produces in-field knowledge for pseudo-labeled examples. The student model uses these pseudo-labels for supervised learning, and the student model and MMS meta-weigher are jointly trained to give each example subtask-specific weights to balance class labels and mitigate the noise effects caused by self-training. The proposed technique is evaluated on a discharge summary dataset against six state-of-the-art approaches, and the results demonstrate that it outperforms these approaches with complete labeled data and achieves results equivalent to state-of-the-art methods that require all labeled data using aspect-based sentiment analysis (ABSA).

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Research Output to Industry Use: A Readiness Study for Topic Modelling with Sentiment Analysis

Cited by 2 publications

References 27 publications

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach

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