scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a CSQ-8 Questionnaire About Mindfulness in Times of Covid-19 to Improve Strategy Generation

2021 ◽  
Author(s):  
Mario Jojoa ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel De la Torre Diez ◽  
Manuel Franco-Martín
Keyword(s):  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text ◽  
Text Data ◽  
Learning Techniques

The aim of this study was to build a tool to analyze, using artificial intelligence, the sentiment perception of users who answered two questions from the CSQ – 8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satis-faction level of the participants involved, with a view to establishing strategies to improve fu-ture experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks and transfer learning, so as to classify the inputs into the following 3 categories: negative, neutral and positive. Due to the lim-ited amount of data available - 86 registers for the first and 68 for the second - transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02 % and 90.53 % respectively based on ground truth labeled by 3 experts. Finally, we proposed a complementary analysis, using com-puter graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

scholarly journals Sentiment of App with Word Vectors

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2156-2159
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Data ◽  
Vector Representations ◽  
Text Sentiment Analysis

Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment words extraction, polarity of sentiment words detection, and text sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. Vector representations has been used to compute various vector-based features and conduct systematically experiments to demonstrate their effectiveness. Using simple vector based features can achieve better results for text sentiment analysis of APP.

Aspect-based sentiment analysis of reviews in the domain of higher education

2020 ◽  
Vol 38 (1) ◽  
pp. 44-64
Author(s):  
Nikola Nikolić ◽  
Olivera Grljević ◽  
Aleksandar Kovačević
Keyword(s):  
Higher Education ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Higher Education Institutions ◽  
Educational Institution ◽  
Teaching Staff ◽  
Free Text ◽  
Content Type ◽  
F Measure

Purpose Student recruitment and retention are important issues for all higher education institutions. Constant monitoring of student satisfaction levels is therefore crucial. Traditionally, students voice their opinions through official surveys organized by the universities. In addition to that, nowadays, social media and review websites such as “Rate my professors” are rich sources of opinions that should not be ignored. Automated mining of students’ opinions can be realized via aspect-based sentiment analysis (ABSA). ABSA s is a sub-discipline of natural language processing (NLP) that focusses on the identification of sentiments (negative, neutral, positive) and aspects (sentiment targets) in a sentence. The purpose of this paper is to introduce a system for ABSA of free text reviews expressed in student opinion surveys in the Serbian language. Sentiment analysis was carried out at the finest level of text granularity – the level of sentence segment (phrase and clause). Design/methodology/approach The presented system relies on NLP techniques, machine learning models, rules and dictionaries. The corpora collected and annotated for system development and evaluation comprise students’ reviews of teaching staff at the Faculty of Technical Sciences, University of Novi Sad, Serbia, and a corpus of publicly available reviews from the Serbian equivalent of the “Rate my professors” website. Findings The research results indicate that positive sentiment can successfully be identified with the F-measure of 0.83, while negative sentiment can be detected with the F-measure of 0.94. While the F-measure for the aspect’s range is between 0.49 and 0.89, depending on their frequency in the corpus. Furthermore, the authors have concluded that the quality of ABSA depends on the source of the reviews (official students’ surveys vs review websites). Practical implications The system for ABSA presented in this paper could improve the quality of service provided by the Serbian higher education institutions through a more effective search and summary of students’ opinions. For example, a particular educational institution could very easily find out which aspects of their service the students are not satisfied with and to which aspects of their service more attention should be directed. Originality/value To the best of the authors’ knowledge, this is the first study of ABSA carried out at the level of sentence segment for the Serbian language. The methodology and findings presented in this paper provide a much-needed bases for further work on sentiment analysis for the Serbian language that is well under-resourced and under-researched in this area.

scholarly journals Sentiment Analysis of Health Care: Review

2021 ◽  
Vol 319 ◽  
pp. 01064
Author(s):  
Issam Aattouchi ◽  
Saida Elmendili ◽  
Fatna Elmendili
Keyword(s):  
Health Care ◽  
Social Media ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
Medical Information ◽  
Free Text ◽  
Medical Problems ◽  
Web Social

Twitter is a microblogging service where users can send and read short messages of 140 characters called “tweets”. Many healthcare-related unstructured and free-text tweets are shared on Twitter, which is becoming a popular domain for medical research. Sentiment analysis is one of the data mining types that provides an estimate of the direction of personality sentiment analysis in natural language processing. By analyzing text, computational linguistics is used to infer and analyze mental knowledge of the web, social media, and related references. The data reviewed actually quantifies the attitudes or feelings of the global society towards specific goods, people, or thoughts and exposes the contextual duality of the knowledge. Sentiment analysis is used in various sectors such as health care. There is an incredible amount of healthcare information available online, such as social media, and websites focused on rating medical problems, that is not accessed in a methodical way. Sentiment analysis has many benefits, such as using medical information to achieve the best possible patient outcome and improve the quality of health care. This review paper focuses on the presented sentiment analysis methods that are used in the medical field.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

scholarly journals A Survey on Twitter Sentimental Analysis with Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 462
Author(s):  
G Krishna Chaitanya ◽  
Dinesh Reddy Meka ◽  
Vakalapudi Surya Vamsi ◽  
M V S Ravi Karthik
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Buying Behavior ◽  
Online Retail ◽  
User Reviews ◽  
Business Plans ◽  
Learning Techniques ◽  
User Data

Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general. 

scholarly journals Identifying Medication-related Intents from a Bidirectional Text Messaging Platform for Hypertension Management: An Unsupervised Learning Approach

2021 ◽  
Author(s):  
Anahita Davoudi ◽  
Natalie Lee ◽  
Thaibinh Luong ◽  
Timothy Delaney ◽  
Elizabeth Asch ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Unsupervised Learning ◽  
Language Processing ◽  
Text Messaging ◽  
Latent Dirichlet Allocation ◽  
Clinical Care ◽  
Hypertension Management ◽  
Free Text ◽  
Significant Heterogeneity ◽  
Text Data

Background: Free-text communication between patients and providers is playing an increasing role in chronic disease management, through platforms varying from traditional healthcare portals to more novel mobile messaging applications. These text data are rich resources for clinical and research purposes, but their sheer volume render them difficult to manage. Even automated approaches such as natural language processing require labor-intensive manual classification for developing training datasets, which is a rate-limiting step. Automated approaches to organizing free-text data are necessary to facilitate the use of free-text communication for clinical care and research. Objective: We applied unsupervised learning approaches to 1) understand the types of topics discussed and 2) to learn medication-related intents from messages sent between patients and providers through a bi-directional text messaging system for managing participant blood pressure. Methods: This study was a secondary analysis of de-identified messages from a remote mobile text-based employee hypertension management program at an academic institution. In experiment 1, we trained a Latent Dirichlet Allocation (LDA) model for each message type (inbound-patient and outbound-provider) and identified the distribution of major topics and significant topics (probability >0.20) across message types. In experiment 2, we annotated all medication-related messages with a single medication intent. Then, we trained a second LDA model (medLDA) to assess how well the unsupervised method could identify more fine-grained medication intents. We encoded each medication message with n-grams (n-1-3 words) using spaCy, clinical named entities using STANZA, and medication categories using MedEx, and then applied Chi-square feature selection to learn the most informative features associated with each medication intent. Results: A total of 253 participants and 5 providers engaged in the program generating 12,131 total messages: 47% patient messages and 53% provider messages. Most patient messages correspond to blood pressure (BP) reporting, BP encouragement, and appointment scheduling. In contrast, most provider messages correspond to BP reporting, medication adherence, and confirmatory statements. In experiment 1, for both patient and provider messages, most messages contained 1 topic and few with more than 3 topics identified using LDA. However, manual review of some messages within topics revealed significant heterogeneity even within single-topic messages as identified by LDA. In experiment 2, among the 534 medication messages annotated with a single medication intent, most of the 282 patient medication messages referred to medication request (48%; n=134) and medication taking (28%; n=79); most of the 252 provider medication messages referred to medication question (69%; n=173). Although medLDA could identify a majority intent within each topic, the model could not distinguish medication intents with low prevalence within either patient or provider messages. Richer feature engineering identified informative lexical-semantic patterns associated with each medication intent class. Conclusion: LDA can be an effective method for generating subgroups of messages with similar term usage and facilitate the review of topics to inform annotations. However, few training cases and shared vocabulary between intents precludes the use of LDA for fully automated deep medication intent classification.

scholarly journals VLSP SHARED TASK: SENTIMENT ANALYSIS

2019 ◽  
Vol 34 (4) ◽  
pp. 295-310 ◽  
Author(s):  
Huyen T M Nguyen ◽  
Hung V Nguyen ◽  
Quyen T Ngo ◽  
Luong X Vu ◽  
Vu Mai Tran ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Objective Evaluation ◽  
Shared Task ◽  
Electronic Products ◽  
Performance Quality ◽  
Benchmark Datasets ◽  
Active Research ◽  
Evaluation Measurement

Sentiment analysis is a natural language processing (NLP) task of identifying orextracting the sentiment content of a text unit. This task has become an active research topic since the early 2000s. During the two last editions of the VLSP workshop series, the shared task on Sentiment Analysis (SA) for Vietnamese has been organized in order to provide an objective evaluation measurement about the performance (quality) of sentiment analysis tools, and encouragethe development of Vietnamese sentiment analysis systems, as well as to provide benchmark datasets for this task. The rst campaign in 2016 only focused on the sentiment polarity classication, with a dataset containing reviews of electronic products. The second campaign in 2018 addressed the problem of Aspect Based Sentiment Analysis (ABSA) for Vietnamese, by providing two datasets containing reviews in restaurant and hotel domains. These data are accessible for research purpose via the VLSP website vlsp.org.vn/resources. This paper describes the built datasets as well as the evaluation results of the systems participating to these campaigns.

scholarly journals SENTIMENT ANALYSIS FOR SARCASTIC MESSAGES IN SOCIAL MEDIA USING DEEP LEARNING TECHNIQUES - AN EMPIRICAL STUDY

2021 ◽  
Vol 9 (2) ◽  
pp. 1051-1052
Author(s):  
K. Kavitha, Et. al.
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Empirical Study ◽  
Sentiment Analysis ◽  
Learning Strategies ◽  
Language Processing ◽  
The People ◽  
Learning Techniques ◽  
Key Terms ◽  
Learning Frameworks

Sentiments is the term of opinion or views about any topic expressed by the people through a source of communication. Nowadays social media is an effective platform for people to communicate and it generates huge amount of unstructured details every day. It is essential for any business organization in the current era to process and analyse the sentiments by using machine learning and Natural Language Processing (NLP) strategies. Even though in recent times the deep learning strategies are becoming more familiar due to higher capabilities of performance. This paper represents an empirical study of an application of deep learning techniques in Sentiment Analysis (SA) for sarcastic messages and their increasing scope in real time. Taxonomy of the sentiment analysis in recent times and their key terms are also been highlighted in the manuscript. The survey concludes the recent datasets considered, their key contributions and the performance of deep learning model applied with its primary purpose like sarcasm detection in order to describe the efficiency of deep learning frameworks in the domain of sentimental analysis.

scholarly journals DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sofia B. Dias ◽  
Sofia J. Hadjileontiadou ◽  
José Diniz ◽  
Leontios J. Hadjileontiadis
Keyword(s):  
Online Learning ◽  
Deep Learning ◽  
Predictive Model ◽  
Short Term Memory ◽  
Ground Truth ◽  
Face To Face ◽  
Learning Techniques ◽  
Learning Plans ◽  
First Time

AbstractCoronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner’s behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users’ interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) $$<0.009$$ < 0.009 , and average correlation coefficient between ground truth and predicted QoI values $$r\ge 0.97$$ r ≥ 0.97 $$(p<0.05)$$ ( p < 0.05 ) , when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user’s online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners’ motivation and participation in the learning process.

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