Sentiment Analysis with Machine Learning Methods on Social Media

Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN

Health is very important for human life. In particular, the health of the brain, which is the executive of the vital resource, is very important. Diagnosis for human health is provided by magnetic resonance imaging (MRI) devices, which help health decision makers in critical organs such as brain health. Images from these devices are a source of big data for artificial intelligence. This big data enables high performance in image processing classification problems, which is a subfield of artificial intelligence. In this study, we aim to classify brain tumors such as glioma, meningioma, and pituitary tumor from brain MR images. Convolutional Neural Network (CNN) and CNN-based inception-V3, EfficientNetB4, VGG19, transfer learning methods were used for classification. F-score, recall, imprinting and accuracy were used to evaluate these models. The best accuracy result was obtained with VGG16 with 98%, while the F-score value of the same transfer learning model was 97%, the Area Under the Curve (AUC) value was 99%, the recall value was 98%, and the precision value was 98%. CNN architecture and CNN-based transfer learning models are very important for human health in early diagnosis and rapid treatment of such diseases.

An Improvised Sentiment Analysis Model on Twitter Data Using Stochastic Gradient Descent (SGD) Optimization Algorithm in Stochastic Gate Neural Network (SGNN)

Sentiment analysis is one of the effective techniques for mining the opinion from shapeless data contains text like review of the products, review of the movie. Sentiment analysis is used as a key to gather response from consumers, reviews of brands, marketing analyses, and political campaigns. In the subject of natural processing, performing sentiment analysis using the data obtained from Twitter is considered as a new study in these days. The dataset is gathered using the Twitter API and the Twitter package. The analysis of Twitter data is a process which takes place automatically by text data analysis to determine the view of public on the specified topic. Here, an improvised sentimental analysis model is proposed to identify the polarity of the tweets such as positive, neutral and negative. In this paper, stochastic gradient descent (SGD) algorithm uses stochastic gradient neural network (SGNN) to categorize the sentiment analysis on basis of tweets provided by the Twitter users and the proposed stochastic gradient descent optimization Algorithm based on stochastic gradient neural network (SGDOA-SGNN) provides better performance when compared with the existing Forest-Whale Optimization Algorithm based on deep neural network F-WOA-DNN model.

Explorations of the Role of Digital Technology in HIV-Related Implementation Research: Case Comparisons of Five Ending the HIV Epidemic Supplement Awards

INTRODUCTION

The use of digital technology in HIV-related interventions and implementation strategies is increasing. Whether the use of technology is to directly improve patient outcomes (ie, part of the intervention) or as part of the strategy to implement interventions has important implications. In this article, we present 5 case studies of projects that feature the use of technology in HIV-related implementation research to identify and describe challenges specific to technology-based implementation research about study design, outcome measurement, implementing in an evolving technology landscape, and equity.

METHODS

For each case study, we identified the technological components, classified the components as intervention or implementation strategy, and identified implications for measuring performance and ensuring equity. The Exploration, Preparation, Implementation, and Sustainment framework was used to identify the research stage of each project.

RESULTS

Technology is being leveraged across a diverse array of implementation strategies to promote Ending the HIV Epidemic in the United States. The case studies were primarily in the exploration and preparation phases of implementation, yet technology played a different role in each project- developing educational materials, mass media to recruit participants or distribute evidence-based campaigns, providing training, guiding tailoring, and implementing novel methods to democratize intervention development.

DISCUSSION

Technology can play multiple roles in HIV-related implementation research projects, including serving as the intervention, being leveraged within implementation strategies, or both. We identified multiple considerations across projects that should be taken into account when measuring success and planning for equitable and sustained impact.

Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model

As people increasingly make hotel booking decisions relying on online reviews, how to effectively improve customer ratings has become a major point for hotel managers. Online reviews serve as a promising data source to enhance service attributes in order to improve online bookings. This paper employs online customer ratings and textual reviews to explore the bidirectional performance (good performance in positive reviews and poor performance in negative reviews) of hotel attributes in terms of four hotel star ratings. Sentiment analysis and a combination of the Kano model and importance-performance analysis (IPA) are applied. Feature extraction and sentiment analysis techniques are used to analyze the bidirectional performance of hotel attributes in terms of four hotel star ratings from 1,090,341 online reviews of hotels in London collected from TripAdvisor.com (accessed on 4 January 2022). In particular, a new sentiment lexicon for hospitality domain is built from numerous online reviews using the PolarityRank algorithm to convert textual reviews into sentiment scores. The Kano-IPA model is applied to explain customers’ rating behaviors and prioritize attributes for improvement. The results provide determinants of high/low customer ratings to different star hotels and suggest that hotel attributes contributing to high/low customer ratings vary across hotel star ratings. In addition, this paper analyzed the Kano categories and priority rankings of six hotel attributes for each star rating of hotels to formulate improvement strategies. Theoretical and practical implications of these results are discussed in the end.