ICSH 2018: LSTM based Sentiment Analysis for Patient Experience Narratives in E-survey Tools

Xia, Chenxi; Zhao, Dong; Wang, Jing; Liu, Jing; Ma, Jingdong

doi:10.1007/978-3-030-03649-2_23

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…This gate is generally called the input gate. The output gate also decides the amount of information from the former and present stage that will be passed to the following stage 60 . Therefore, LSTM works as follows 59 :…”

Section: Neural Network For Encoding Wrong Wordsmentioning

confidence: 99%

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Correcting spelling mistakes in Persian texts with rules and deep learning methods

Kasmaiee,

Homayounpour

2023

Sci Rep

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This study aims to develop a system for automatically correcting spelling errors in Persian texts using two approaches: one that relies on rules and a common spelling mistake list and another that uses a deep neural network. The list of 700 common misspellings was compiled, and a database of 55,000 common Persian words was used to identify spelling errors in the rule-based approach. 112 rules were implemented for spelling correction, each providing suggested words for misspelled words. 2500 sentences were used for evaluation, with the word with the shortest Levenshtein distance selected for evaluation. In the deep learning approach, a deep encoder-decoder network that utilized long short-term memory (LSTM) with a word embedding layer was used as the base network, with FastText chosen as the word embedding layer. The base network was enhanced by adding convolutional and capsule layers. A database of 1.2 million sentences was created, with 800,000 for training, 200,000 for testing, and 200,000 for evaluation. The results showed that the network's performance with capsule and convolutional layers was similar to that of the base network. The network performed well in evaluation, achieving accuracy, precision, recall, F-measure, and bilingual evaluation understudy (Bleu) scores of 87%, 70%, 89%, 78%, and 84%, respectively.

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Section: Neural Network For Encoding Wrong Wordsmentioning

confidence: 99%

“…f t and i t are activation vectors of forget and input gates, and c t is the vector that feeds into the cell. These parameters are obtained from the following relations 60 :…”

Section: Neural Network For Encoding Wrong Wordsmentioning

confidence: 99%

Correcting spelling mistakes in Persian texts with rules and deep learning methods

Kasmaiee,

Homayounpour

2023

Sci Rep

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“…This process typically involves the classification of words as expressing either positive or negative polarity, and numerous resources have been developed for this task in nonclinical domains, such as customer reviews [8][9][10][11] and social media [12][13][14]. Research efforts have also focused on the analysis of sentiment within health care-related texts, such as patient feedback forms [15,16], online forums [17], and social networks [18,19].…”

Section: Introductionmentioning

confidence: 99%

Using General-purpose Sentiment Lexicons for Suicide Risk Assessment in Electronic Health Records: Corpus-Based Analysis

Bittar¹,

Velupillai²,

Roberts³

et al. 2021

JMIR Med Inform

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Background Suicide is a serious public health issue, accounting for 1.4% of all deaths worldwide. Current risk assessment tools are reported as performing little better than chance in predicting suicide. New methods for studying dynamic features in electronic health records (EHRs) are being increasingly explored. One avenue of research involves using sentiment analysis to examine clinicians’ subjective judgments when reporting on patients. Several recent studies have used general-purpose sentiment analysis tools to automatically identify negative and positive words within EHRs to test correlations between sentiment extracted from the texts and specific medical outcomes (eg, risk of suicide or in-hospital mortality). However, little attention has been paid to analyzing the specific words identified by general-purpose sentiment lexicons when applied to EHR corpora. Objective This study aims to quantitatively and qualitatively evaluate the coverage of six general-purpose sentiment lexicons against a corpus of EHR texts to ascertain the extent to which such lexical resources are fit for use in suicide risk assessment. Methods The data for this study were a corpus of 198,451 EHR texts made up of two subcorpora drawn from a 1:4 case-control study comparing clinical notes written over the period leading up to a suicide attempt (cases, n=2913) with those not preceding such an attempt (controls, n=14,727). We calculated word frequency distributions within each subcorpus to identify representative keywords for both the case and control subcorpora. We quantified the relative coverage of the 6 lexicons with respect to this list of representative keywords in terms of weighted precision, recall, and F score. Results The six lexicons achieved reasonable precision (0.53-0.68) but very low recall (0.04-0.36). Many of the most representative keywords in the suicide-related (case) subcorpus were not identified by any of the lexicons. The sentiment-bearing status of these keywords for this use case is thus doubtful. Conclusions Our findings indicate that these 6 sentiment lexicons are not optimal for use in suicide risk assessment. We propose a set of guidelines for the creation of more suitable lexical resources for distinguishing suicide-related from non–suicide-related EHR texts.

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Identifying suicidal emotions on social media through transformer-based deep learning

Dheeraj

Ramakrishnudu

2022

Appl Intell

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ICSH 2018: LSTM based Sentiment Analysis for Patient Experience Narratives in E-survey Tools

Cited by 5 publications

References 35 publications

Correcting spelling mistakes in Persian texts with rules and deep learning methods

Correcting spelling mistakes in Persian texts with rules and deep learning methods

Using General-purpose Sentiment Lexicons for Suicide Risk Assessment in Electronic Health Records: Corpus-Based Analysis

Identifying suicidal emotions on social media through transformer-based deep learning

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