Sentiment Analysis Approach Research Articles

Sentiment analysis of clinical narratives: A scoping review

A clinical sentiment is a judgment, thought or attitude promoted by an observation with respect to the health of an individual. Sentiment analysis has drawn attention in the healthcare domain for secondary use of data from clinical narratives, with a variety of applications including predicting the likelihood of emerging mental illnesses or clinical outcomes. The current state of research has not yet been summarized. This study presents results from a scoping review aiming at providing an overview of sentiment analysis of clinical narratives in order to summarize existing research and identify open research gaps. The scoping review was carried out in line with the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guideline. Studies were identified by searching 4 electronic databases (e.g., PubMed, IEEE Xplore) in addition to conducting backward and forward reference list checking of the included studies. We extracted information on use cases, methods and tools applied, used datasets and performance of the sentiment analysis approach. Of 1,200 citations retrieved, 29 unique studies were included in the review covering a period of 8 years. Most studies apply general domain tools (e.g. TextBlob) and sentiment lexicons (e.g. SentiWordNet) for realizing use cases such as prediction of clinical outcomes; others proposed new domain-specific sentiment analysis approaches based on machine learning. Accuracy values between 71.5–88.2% are reported. Data used for evaluation and test are often retrieved from MIMIC databases or i2b2 challenges. Latest developments related to artificial neural networks are not yet fully considered in this domain. We conclude that future research should focus on developing a gold standard sentiment lexicon, adapted to the specific characteristics of clinical narratives. Efforts have to be made to either augment existing or create new high-quality labeled data sets of clinical narratives. Last, the suitability of state-of-the-art machine learning methods for natural language processing and in particular transformer-based models should be investigated for their application for sentiment analysis of clinical narratives.

A natural language processing approach to uncover patterns among online ratings of otolaryngologists.

Patients increasingly use physician rating websites to evaluate and choose potential healthcare providers. A sentiment analysis and machine learning approach can uniquely analyse written prose to quantitatively describe patients' perspectives from interactions with their physicians. Online written reviews and star scores were analysed from Healthgrades.com using a natural language processing sentiment analysis package. Demographics of otolaryngologists were compared and a multivariable regression for individual words was performed. This study analysed 18 546 online reviews of 1240 otolaryngologists across the USA. Younger otolaryngologists (aged less than 40 years) had higher sentiment and star scores compared with older otolaryngologists (p < 0.001). Male otolaryngologists had higher sentiment and star scores compared with female otolaryngologists (p < 0.001). 'Confident', 'kind', 'recommend' and 'comfortable' were words associated with positive reviews (p < 0.001). Positive bedside manner was strongly reflected in better reviews, and younger age and male gender of the otolaryngologist were associated with better sentiment and star scores.

Evaluation of Deep Learning Approaches for Sentiment Analysis

Due to the increasing popularity of posting evaluations, sentiment analysis has grown to be a crucial area of study. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and GRU (Gated Recurrent Unit). Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. To discover the optimal deep learning methodology for the given data, authors here proposed many deep learning methodologies for text data on sentiment analysis. A publicly available dataset including both positive and negative reviews on LSTM, CNN, RNN, and GRU was used in the experiments, and the findings showed that CNN had the highest accuracy compared to the other models. Based on the experimental results of CNN, it was found that prediction from the proposed work exhibited a significant improvement over existing work.

The Influence of Social Media on Cryptocurrency Price: A Sentiment Analysis Approach

The Influence of Social Media on Cryptocurrency Price: A Sentiment Analysis Approach

Curtailing fake news creation and dissemination in Nigeria: Twitter social network and sentiment analysis approaches

Influencers create and facilitate dissemination of information in a network and can shape the attitudes and beliefs of members of a social network. Identifying the influencers and their relations, as well as the sentiment of tweets being disseminated in the network on some selected keywords can help curtail fake news creation and dissemination in the network. This study uses a sequential mixed-methods design with a quantitative method followed by qualitative methods. The quantitative data were collected using Mozdeh big data analysis software. Mozdeh software was used to collect tweets through Twitter’s Streaming application programming interface to build a corpus of tweets, collected from 1 January 2016 to 30 June 2021. The study found two major actors/influencers involved in the creation and dissemination of fake news on the tweeter social network studied. The study further found overall Av. Pos. − Av. Neg. was −0.9935. Data collected were on some specific trending keywords on a particular region in Nigeria. Identifying and monitoring the tweets of influencers in a network can aid in debunking fake news immediately after dissemination and discourage the use of offensive words in tweets. The results revealed major influencers responsible for creating and disseminating fake news on some trending issues in Nigeria.

THE RELEVANCE OF SUSTAINABILITY IN DAY TO LIVING BY WAY OF ORGANIC CONSUMPTION: A DETAILED STUDY ON THE CONSUMER BUYING BEHAVIOUR OF ORGANIC PRODUCTS IN MUMBAI.

Organic products like body care or food products are trending in the market for their originality, nutritional value and long term benefits to its users or consumers. Though this trend is observed we are keen to know about the exact sentiments or feelings of consumers while purchasing the products and after using them. Are these products successful in building their organic value and consumers positive perception towards them? What are the trust building or reducing factors for these organic products? And many other aspects of consumer behavior in order to understand their motive and motivation behind selecting organic products and services. This paper is our sincere effort towards understanding the position of these organic products in consumers mind and in actual market through the consumer behavior sentiment analysis approach. KEYWORDS: Organic product, Consumer Behavior, Health benefits, Consumer Perception, Organic Retailing. Organic food, Sustainable living.

Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques.

The outbreak of the COVID-19 pandemic has also triggered a tsunami of news, instructions, and precautionary measures related to the disease on social media platforms. Despite the considerable support on social media, a large number of fake propaganda and conspiracies are also circulated. People also reacted to COVID-19 vaccination on social media and expressed their opinions, perceptions, and conceptions. The present research work aims to explore the opinion dynamics of the general public about COVID-19 vaccination to help the administration authorities to devise policies to increase vaccination acceptance. For this purpose, a framework is proposed to perform sentiment analysis of COVID-19 vaccination-related tweets. The influence of term frequency-inverse document frequency, bag of words (BoW), Word2Vec, and combination of TF-IDF and BoW are explored with classifiers including random forest, gradient boosting machine, extra tree classifier (ETC), logistic regression, Naïve Bayes, stochastic gradient descent, multilayer perceptron, convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and recurrent neural network (RNN). Results reveal that ETC outperforms using BoW with a 92% of accuracy and is the most suitable approach for sentiment analysis of COVID-19-related tweets. Opinion dynamics show that sentiments in favor of vaccination have increased over time.

Improving Sentiment Prediction of Textual Tweets Using Feature Fusion and Deep Machine Ensemble Model

Widespread fear and panic has emerged about COVID-19 on social media platforms which are often supported by falsified and altered content. This mass hysteria creates public anxiety due to misinformation, misunderstandings, and ignorance of the impact of COVID-19. To assist health professionals in addressing this epidemic more appropriately at the onset, sentiment analysis can potentially help the authorities for devising appropriate strategies. This study analyzes tweets related to COVID-19 using a machine learning approach and offers a high-accuracy solution. Experiments are performed involving different machine and deep learning models along with various features such as Word2vec, term-frequency, term-frequency document frequency, and feature fusion of both feature-generating approaches. The proposed approach combines the extra tree classifier and convolutional neural network and uses feature fusion to achieve the highest accuracy score of 99%. The proposed approach obtains far better results than existing sentiment analysis approaches.

Sentiment analysis and emotion detection of post-COVID educational Tweets: Jordan case.

Education evolved dramatically under Covid-19, and owing to the conditions, distant learning became mandatory. However, this has opened new realities to the educational business under the label of "Hybrid-Learning," where educational institutions are still using online learning in addition to face-to-face learning, which has changed people's lives and split their opinions and emotions. As a result, this study investigated the Jordanian community's perspectives and feelings on the transition from pure face-to-face education to blended education by examining related tweets in the post-COVID era. Specifically, using NLP Emotion detection and Sentiment Analysis approaches, as well as deep learning models. As a result of analyzing the collected tweets, 18.75% of studied Jordanian's community sample are dissatisfied (Anger and Hate), 21.25% are negative (Sad), 13% are Happy, and 24.50 percent are Neutral about it.

An enhanced approach for sentiment analysis based on meta-ensemble deep learning

Sentiment analysis, commonly known as “opinion mining,” aims to identify sentiment polarities in opinion texts. Recent years have seen a significant increase in the acceptance of sentiment analysis by academics, businesses, governments, and several other organizations. Numerous deep-learning efforts have been developed to effectively handle more challenging sentiment analysis problems. However, the main difficulty with deep learning approaches is that they require a lot of experience and hard work to tune the optimal hyperparameters, making it a tedious and time-consuming task. Several recent research efforts have attempted to solve this difficulty by combining the power of ensemble learning and deep learning. Many of these efforts have concentrated on simple ensemble techniques, which have some drawbacks. Therefore, this paper makes the following contributions: First, we propose a meta-ensemble deep learning approach to improve the performance of sentiment analysis. In this approach, we train and fuse baseline deep learning models using three levels of meta-learners. Second, we propose the benchmark dataset “Arabic-Egyptian Corpus 2” as an extension of a previous corpus. The corpus size has been increased by 10,000 annotated tweets written in colloquial Arabic on various topics. Third, we conduct several experiments on six benchmark datasets of sentiment analysis in different languages and dialects to evaluate the performance of the proposed meta-ensemble deep learning approach. The experimental results reveal that the meta-ensemble approach effectively outperforms the baseline deep learning models. Also, the experiments reveal that meta-learning improves performance further when the probability class distributions are used to train the meta-learners.

Sentiment Analysis in Multiple Languages: A Review of Current Approaches and Challenges

Sentiment analysis, the process of automatically identifying and extracting subjective information from text, has gained increasing attention in recent years due to its potential applications in a variety of fields. However, the task of sentiment analysis can be challenging when applied to texts in multiple languages, as it requires not only language-specific preprocessing and feature extraction techniques, but also the development and adaptation of machine learning models that are able to handle the complexities of different languages. This research paper provides an overview of the current approaches and challenges in sentiment analysis for multiple languages. This study begins by discussing the general principles and techniques of sentiment analysis, including the use of deep learning and machine learning methods, as well as the importance of feature selection and ethical considerations. It examines the specific challenges and approaches for sentiment analysis in various languages, including Arabic, Chinese, Russian, and English. The use of multimodal sentiment analysis and the potential applications of sentiment analysis in various domains, such as healthcare, social media, and customer service. At the end, this review highlights the potential of sentiment analysis in multiple languages and the need for further research to improve the accuracy and reliability of sentiment analysis models for a variety of languages and domains. Future work should also address the ethical concerns involved in the collection and use of sentiment analysis data, as well as the challenges of adapting models to new languages and domains.

A hybrid composite features based sentence level sentiment analyzer

&lt;div align="left"&gt;&lt;span lang="EN-US"&gt;Current lexica and machine learning based sentiment analysis approaches still suffer from a two-fold limitation. First, manual lexicon construction and machine training is time consuming and error-prone. Second, the prediction’s accuracy entails sentences and their corresponding training text should fall under the same domain. In this article, we experimentally evaluate four sentiment classifiers, namely Support Vector Machines, Naive Bayes, Logistic Regression and Random Forest. We quantify the quality of each of these models using three real-world datasets that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic movie reviews. Specifically, we study the impact of a variety of natural language processing (NLP) pipelines on the quality of the predicted sentiment orientations. Additionally, we measure the impact of incorporating lexical semantic knowledge captured by WordNet on expanding original words in sentences. Findings demonstrate that the utilizing different NLP pipelines and semantic relationships impacts the quality of the sentiment analyzers. In particular, results indicate that coupling lemmatization and knowledge-based n-gram features proved to produce higher accuracy results. With this coupling, the accuracy of the support vector machine (SVM) classifier has improved to 90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three other classifiers. &lt;/span&gt;&lt;/div&gt;

A new sentiment analysis method to detect and Analyse sentiments of Covid-19 moroccan tweets using a recommender approach.

Since the beginning of the covid-19 crisis, people from all over the world have used social media platforms to publish their opinions, sentiments, and ideas about the coronavirus epidemic and their news. Due to the nature of social networks, users share an immense amount of data every day in a freeway, which gives them the possibility to express opinions and sentiments about the coronavirus pandemic regardless of the time and the place. Moreover, The rapid number of exponential cases globally has become the apprehension of panic, fear, and anxiety among people. In this paper, we propose a new sentiment analysis approach to detect sentiments in Moroccan tweets related to covid-19 from March to October 2020. The proposed model is a recommender approach using the advantages of recommendation systems for classifying each tweet into three classes: positive, negative, or neutral. Experimental results show that our method gives good accuracy(86%) and outperforms the well-known machine learning algorithms. We find also that the sentiments of users changed from period to period, and that the evolution of the epidemiological situation in morocco affects the sentiments of users.

Eurocentric cultural theme parks in Japan: domestic tourists’ perspectives on place branding

ABSTRACT During the Bubble Economy era, cultural theme parks were constructed in rural Japan for economic rejuvenation. These cultural theme parks or gaikoku mura (foreign villages) embrace foreign cultures and landscapes rather than rollercoasters. They offer Japanese tourists the foreign travel experience adapted and modified for the Japanese market. Set within place-based branding, imaginaries, and authenticity, this paper compares the Eurocentric theme parks of Huis Ten Bosch in Nagasaki and Yufuin Floral Village in Oita. Opening in 1992, Huis Ten Bosch is a large-scale cultural theme park based on Nagasaki’s historical connection to the Netherlands. Recreating Dutch cities and landscapes, it offers hotels, restaurants, shops, and attractions. Within the hot springs area of Yufuin, the Floral Village opened in 2012, modelled after The Cotswolds, UK. Houses with storybook architecture contain souvenir shops and tea rooms featuring characters from British literature and Studio Ghibli anime. Based on site visits, photographic documentation, and qualitative sentiment analysis of comments written by visitors on TripAdvisor, the paper investigates the perceptions of Japanese tourists. They are positive about Huis Ten Bosch with developers having successfully created and branded a Dutch environment, while mixed messages at Yufuin demonstrate the importance of product-market match for these ‘exotic’ attractions.

Lexicon-based sentiment analysis in texts using Formal Concept Analysis

In this paper, we present a novel approach for sentiment analysis that uses Formal Concept Analysis (FCA) to create dictionaries for classification. Unlike other methods that rely on pre-defined lexicons, our approach allows for the creation of customised dictionaries that are tailored to the specific data and tasks. By using a dataset of tweets categorised into positive and negative polarity, we show that our approach achieves a better performance than other standard dictionaries.

A Sentiment Analysis Approach on the Acceptance of Radio Frequency Identification Technology-Based Solutions among Schools in Nueva Ecija, Philippines

Integrating new technologies in schools improves different processes and increases trust and confidence from various stakeholders. Advancements in education are a 21st-century goal that every learning institution should consider. This study aims to conduct sentiment analysis on the acceptance of Radio Frequency Identification technology-based solutions among the private and public learning institutions in Nueva Ecija, Philippines, that have implemented the technology. School administrators and teachers participated in the study. Sentiments were analyzed using open-ended questions relating to their acceptance of the technology. Based on the analysis, more favourable terms occurred in their responses than negative words, as presented using a word cloud. The researcher thinks that school administrators and teachers of today are more open to technological progress and integrating technologies into education to improve the quality of processes, improve the delivery of instruction, create a safer and more secure learning environment, and encourage the use of new technologies to raise the quality of teaching and learning.

A lexicon-based approach for sentiment analysis of multimodal content in tweets

A lexicon-based approach for sentiment analysis of multimodal content in tweets

Analisa Sentimen Pengguna Transportasi Jakarta Terhadap Transjakarta Menggunakan Metode Naives Bayes dan K-Nearest Neighbor

Social media used in communicating that is very popular in Indonesia. One of the most popular is Twitter. Twitter is a social media site where people can share information publicly. This information can be processed to make sentiment analysis. This research attempts to create a system that can detect positive or negative sentiments in public information. The method used for this sentiment classification is the comparison method of Naive Bayes Classifier and K-Nearest Neighbor Classifier using TF-IDF weighting. The input to this system is in the form of tweet data for Transjakarta, while the output of this system is in the form of visualization of positive and negative sentiment data using Streamlit which is a library from python. Based on testing the accuracy of the Naive Bayes approach for sentiment analysis of Twitter data related to the use of Transjakarta transportation is 61.1%, and the accuracy of the K-Nearest Neighbor method is 75.7%. For the two methods used in determining the level of accuracy, it can be concluded that the K-nearest-neighbor method produces better accuracy.

A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis

There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as Instagram and Facebook, e-commerce websites like Amazon, Google searches, news publishing websites, and a variety of additional sources are the possible suppliers. Every instant, all these sources produce massive amounts of text data. The interpretation of such data can help business owners analyze the social outlook of their product, brand, or service and take necessary steps. The development of a consumer review summarization model using Natural Language Processing (NLP) techniques and Long short-term memory (LSTM) to present summarized data and help businesses obtain substantial insights into their consumers' behavior and choices is the topic of this research. A hybrid approach for analyzing sentiments is presented in this paper. The process comprises pre-processing, feature extraction, and sentiment classification. Using NLP techniques, the pre-processing stage eliminates the undesirable data from input text reviews. For extracting the features effectively, a hybrid method comprising review-related features and aspect-related features has been introduced for constructing the distinctive hybrid feature vector corresponding to each review. The sentiment classification is performed using the deep learning classifier LSTM. We experimentally evaluated the proposed model using three different research datasets. The model achieves the average precision, average recall, and average F1-score of 94.46%, 91.63%, and 92.81%, respectively.

Sentiment Analysis to Predict Movies Success Rate Based on NLTK Movie Review Corpora Using Machine Learning

With the proliferation of social networks, peoples express their opinions about different things or issues on social media without any hesitation. The rapid growth of textual data on social media are required to develop algorithms and techniques for recognizing people’s opinions towards specific subject. These opinions are helpful in business plans development, marketing trends, political parties’ popularity. The film industry can be an important revenue generating industry of any country. Peoples express their opinion on movie trailer using social media. The effective sentiment analysis of opinions on social media such as Twitter can be helpful to predict movie ratings. This research work focuses on developing a technique to predict movie success rate on the basis of tweets data. We have collected tweets about different movies after their trailer released by using hash tag method. We applied Sentiment analysis approach using Machine learning. In this study we utilized four key algorithms (Naïve Bayes, SVM, Neural Networks, decision tree) on NLTK Movie review corpora.