scholarly journals Identificação de Perfis Depressivos em Redes Sociais Utilizando Aprendizado de Máquina: um Mapeamento Sistemático

2020 ◽  
Author(s):  
Vinicius Casani ◽  
Rafael Gomes Mantovani ◽  
Alinne Cristinne Correa Souza ◽  
Francisco Carlos Monteiro Souza
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Natural Language Processing ◽  
Social Class ◽  
Natural Language ◽  
Language Processing ◽  
Psychological Disorder ◽  
The Social

Depression is a psychological disorder that affects millions of peoplein theworld, regardless of their age, social class or nationality. In theliterature, different techniques have been studying to analyze andrecognize this disease such as Natural Language Processing, SentimentAnalysis, and Machine Learning. In this paper, we describe asystematic mapping to identify evidence regarding techniques thatare often used to identify depressive profiles.We analyzed 472 studiesand we selected 25 primary studies. These studies indicate thatthe SVM and NB techniques have been most used to detect possibledepressive profiles in social networks. Furthermore, Twitter andFacebook with 35,5% and 22,6%, respectively were the social mediamost have been used by users’ express their feelings regarding themost varied subjects.

Identifying Suggestions in Airline-User Tweets Using Natural Language Processing and Machine Learning

2021 ◽  
pp. 481-498
Author(s):  
Rafael Jiménez ◽  
Vicente García ◽  
Karla Olmos-Sánchez ◽  
Alan Ponce ◽  
Jorge Rodas-Osollo
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Many ◽  
Processing Techniques

Social networks have moved from online sites to interact with your friends to a platform where people, artists, brands, and even presidents interact with crowds of people daily. Airlines are some of the companies that use social networks such as Twitter to communicate with their clients through messages with offers, travel recommendations, videos of collaborations with YouTubers, and surveys. Among the many responses to airline tweets, there are users' suggestions on how to improve their services or processes. These recommendations are essential since the success of many companies is based on offering what the client wants or needs. A database of tweets was created using user tweets sent to airline accounts on Twitter between July 30 (2019) and August 8 (2019). Natural language processing techniques were used on the database to preprocess its data. The latest classification results using Naive Bayes show an accuracy of 72.44%.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

scholarly journals Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

2021 ◽  
Vol 28 (1) ◽  
pp. e100262
Author(s):  
Mustafa Khanbhai ◽  
Patrick Anyadi ◽  
Joshua Symons ◽  
Kelsey Flott ◽  
Ara Darzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Patient Experience ◽  
Language Processing ◽  
Performance Metrics ◽  
Free Text ◽  
Patient Feedback

ObjectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.MethodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.ResultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.ConclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.

scholarly journals Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics

2019 ◽  
pp. 1-8 ◽  
Author(s):  
Tomasz Oliwa ◽  
Steven B. Maron ◽  
Leah M. Chase ◽  
Samantha Lomnicki ◽  
Daniel V.T. Catenacci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Named Entity ◽  
Pathology Reports

PURPOSE Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions. PATIENTS AND METHODS Pathology reports from patients with gastroesophageal cancer enrolled in The University of Chicago GI oncology tumor bank were used to train and validate a novel composite natural language processing-based pipeline with a supervised machine learning classification step to separate notes into internal (primary review) and external (consultation) reports; a named-entity recognition step to obtain label (accession number), location, date, and sublabels (block identifiers); and a results proofreading step. RESULTS We analyzed 188 pathology reports, including 82 internal reports and 106 external consult reports, and successfully extracted named entities grouped as sample information (label, date, location). Our approach identified up to 24 additional unique samples in external consult notes that could have been overlooked. Our classification model obtained 100% accuracy on the basis of 10-fold cross-validation. Precision, recall, and F1 for class-specific named-entity recognition models show strong performance. CONCLUSION Through a combination of natural language processing and machine learning, we devised a re-implementable and automated approach that can accurately extract specimen attributes from semistructured pathology notes to dynamically populate a tumor registry.

scholarly journals A Survey on Bias in Deep NLP

2021 ◽  
Vol 11 (7) ◽  
pp. 3184
Author(s):  
Ismael Garrido-Muñoz  ◽  
Arturo Montejo-Ráez  ◽  
Fernando Martínez-Santiago  ◽  
L. Alfonso Ureña-López 
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Probability Distribution ◽  
Natural Language ◽  
Network Design ◽  
Language Processing ◽  
Deep Neural Networks ◽  
Learning Processes ◽  
Relevant Issue

Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as “pre-training”), versatile and performing models are released continuously for every new network design. These networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have been found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. In addition, available resources are identified and a strategy to deal with bias in deep NLP is proposed.

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