Enhancing the Classification Accuracy in Sentiment Analysis with Computational Intelligence using Joint Sentiment Topic Detection with MEDLDA

Kalaivaani, Pcd; Thangarajan, R.

doi:10.31209/2019.100000152

Cited by 11 publications

(12 citation statements)

References 7 publications

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“…It is expected that the produced topics are dependent on the sentiment distributions and that the generated words are conditional on the sentiment topic pairings. Thus, a weakly supervised joint sentiment-topic mode may be utilized to improve the accuracy of topic modeling by extending the maximum entropy discrimination latent Dirichlet allocation (MEDLDA) topic model [ 86 ].…”

Section: Discussionmentioning

confidence: 99%

Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews

Rahim

Ibrahim

Musa

et al. 2021

IJERPH

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Social media is emerging as a new avenue for hospitals and patients to solicit input on the quality of care. However, social media data is unstructured and enormous in volume. Moreover, no empirical research on the use of social media data and perceived hospital quality of care based on patient online reviews has been performed in Malaysia. The purpose of this study was to investigate the determinants of positive sentiment expressed in hospital Facebook reviews in Malaysia, as well as the association between hospital accreditation and sentiments expressed in Facebook reviews. From 2017 to 2019, we retrieved comments from 48 official public hospitals’ Facebook pages. We used machine learning to build a sentiment analyzer and service quality (SERVQUAL) classifier that automatically classifies the sentiment and SERVQUAL dimensions. We utilized logistic regression analysis to determine our goals. We evaluated a total of 1852 reviews and our machine learning sentiment analyzer detected 72.1% of positive reviews and 27.9% of negative reviews. We classified 240 reviews as tangible, 1257 reviews as trustworthy, 125 reviews as responsive, 356 reviews as assurance, and 1174 reviews as empathy using our machine learning SERVQUAL classifier. After adjusting for hospital characteristics, all SERVQUAL dimensions except Tangible were associated with positive sentiment. However, no significant relationship between hospital accreditation and online sentiment was discovered. Facebook reviews powered by machine learning algorithms provide valuable, real-time data that may be missed by traditional hospital quality assessments. Additionally, online patient reviews offer a hitherto untapped indication of quality that may benefit all healthcare stakeholders. Our results confirm prior studies and support the use of Facebook reviews as an adjunct method for assessing the quality of hospital services in Malaysia.

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Section: Discussionmentioning

confidence: 99%

Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews

Rahim

Ibrahim

Musa

et al. 2021

IJERPH

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“…KDD has been a research hotspot in modern biomedicine field for many years, but its application in TCM has been highlighted in recent years [1][2][3][4][21][22][23]. Lukman et al [24] surveyed the progress of computational approaches for TCM formulation and diagnosis mining.…”

Section: Related Workmentioning

confidence: 99%

Mining Syndrome Differentiating Principles from Traditional Chinese Medicine Clinical Data

Ma¹,

Wang²,

Hai³

et al. 2022

Computer Systems Science and Engineering

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Syndrome differentiation-based treatment is one of the key characteristics of Traditional Chinese Medicine (TCM). The process of syndrome differentiation is difficult and challenging due to its complexity, diversity and vagueness. Analyzing syndrome principles from historical records of TCM using data mining (DM) technology has been of high interest in recent years. Nevertheless, in most relevant studies, existing DM algorithms have been simply developed for TCM mining, while the combination of TCM theories or its characteristics with DM algorithms has rarely been reported. This paper presents a novel Symptom-Syndrome Topic Model (SSTM), which is a supervised probabilistic topic model with three-tier Bayesian structure. In the SSTM, syndromes are considered as observed topic labels to distinguish certain symptoms from possible symptoms according to their different positions. The generation of our model is in full compliance with the syndrome differentiation theory of TCM. Experimental results show that the SSTM is more effective than other models for syndrome differentiating.

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“…Table 4 points out the different evaluation parameters used in the reviewed studies, as well as the time frame of the prediction. For the most part, the studies used accuracy as an evaluation metric, which is the percentage ratio of correct predictions over the total number of test instances [146].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

et al. 2021

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With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.

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Enhancing the Classification Accuracy in Sentiment Analysis with Computational Intelligence using Joint Sentiment Topic Detection with MEDLDA

Cited by 11 publications

References 7 publications

Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews

Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews

Mining Syndrome Differentiating Principles from Traditional Chinese Medicine Clinical Data

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

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