scholarly journals Twitter analysis of the orthodontic patient experience with braces vs Invisalign

2016 ◽  
Vol 87 (3) ◽  
pp. 377-383 ◽  
Author(s):  
Daniel Noll ◽  
Brendan Mahon ◽  
Bhavna Shroff ◽  
Caroline Carrico ◽  
Steven J. Lindauer
Keyword(s):  
Sentiment Analysis ◽  
Patient Experience ◽  
Large Scale ◽  
Specific Content ◽  
Significant Difference ◽  
Data Collection Program ◽  
Twitter Analysis ◽  
Twitter Users ◽  
Negative Sentiment ◽  
Collection Program

ABSTRACT Objective: To examine the orthodontic patient experience having braces compared with Invisalign by means of a large-scale Twitter sentiment analysis. Materials and Methods: A custom data collection program was created that collected tweets containing the words “braces” or “Invisalign” for a period of 5 months. A hierarchal Naïve Bayes sentiment analysis classifier was developed to sort the tweets into five categories: positive, negative, neutral, advertisement, or not applicable. Each category was then analyzed for specific content. Results: A total of 419,363 tweets applicable to orthodontics were collected. Users posted significantly more positive tweets (61%) than they did negative tweets (39%; P ≤ .0001). There was no significant difference in the distribution of positive and negative sentiment between braces and Invisalign tweets (P = .4189). Positive orthodontics-related tweets often highlighted gratitude for a great smile accompanied with selfies. Negative orthodontic tweets frequently focused on pain. Conclusion: Twitter users expressed more positive than negative sentiment about orthodontic treatment with no significant difference in sentiment between braces and Invisalign tweets.

scholarly journals Analysis of User Sentiment of Twitter to Draft KUHP

2020 ◽  
Vol 16 (3) ◽  
pp. 273
Author(s):  
Nawang Indah Cahyaningrum ◽  
Danty Welmin Yoshida Fatima ◽  
Wisnu Adi Kusuma ◽  
Sekar Ayu Ramadhani ◽  
Muhammad Rizqi Destanto ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
The Other ◽  
Mining Methods ◽  
Twitter Users ◽  
Negative Sentiment ◽  
Bahasa Indonesia

Twitter is one of social media where its user can share many responses for a phenomenon through a tweet. This research used 5000 tweets from Twitter users in Bahasa Indonesia with keyword “RUU KUHP(Draft Law of KUHP)” from 16th of September until 22nd of September 2019. That tweets were processed using Rstudio software with sentiment analysis that is one of Text Mining methods. This research aims to classify Twitter users’ responses to RUU KUHP to be negative sentiment, poisitive negative, and neutral. Also, this research also aims to know about topics’ frequencies that were related to RUU KUHP through visualization with bar plot and also wordcloud. This research also aims to know words that are associated with the most frequent words. Form this research, can be known that Twitter users’ responses to RUU KUHP tend to have neutral sentiment that means they did not take side between agreeing or disagreeing. From this research, also can be known about 10 most frequent words, there are kpk, tunda, dpr, pasal, kesal, jokowi, presiden, masuk, ya, and sahkan. Beside that, can be known the other words that are associated with them and also their probability.

scholarly journals Sentiment Analysis of West Java International Airport (Bijb) Kertajati on Twitter

2020 ◽  
Vol 4 (2) ◽  
pp. 176-182
Author(s):  
Oka Intan ◽  
Sri Widiyanesti
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Rapid Development ◽  
West Java ◽  
Word Cloud ◽  
Twitter User ◽  
The Social ◽  
Twitter Users ◽  
Negative Sentiment

The rapid development of technology allows everything to accessed by the internet that causes many users of social media and one of the social media is Twitter. An interesting topic to discuss on Twitter is about new and fresh things that attract many users to get involved. One of the things that attract Twitter users is the construction of a new airport, namely Kertajati Airport, which has some problems with airport activities, such as the small number of visitors, lonely conditions of the airport, and decreased number of routes. This study aims to find out Twitter user sentiments towards Kertajati Airport in West Java to know the quality of Kertajati Airport. The method used in this study is sentiment analysis by looking at the calculation of how many positive and negative sentiment have been obtained with the most result so it can reflect the quality of Kertajati Airport and then there is a word cloud to see the spread of word related to sentiment. The results of this study indicate that the quality of the Kertajati Airport cannot be said to be good because the results of the sentiment analysis found that negative sentiments have more percentages than positive sentiments

Tracking Public Attitudes toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-based Sentiment Analysis (Preprint)

2021 ◽  
Author(s):  
Hyeju Jang ◽  
Emily Rempel ◽  
Ian Roe ◽  
Giuseppe Carenini ◽  
Naveed Zafar Janjua
Keyword(s):  
Public Health ◽  
Sentiment Analysis ◽  
Vaccine Hesitancy ◽  
Public Health Response ◽  
Major Barrier ◽  
Positive Sentiment ◽  
Twitter Users ◽  
Key Aspects ◽  
Negative Sentiment ◽  
Health Experts

BACKGROUND The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation poses a major barrier to achieving herd immunity. OBJECTIVE We aim to investigate Twitter users’ attitudes toward COVID-19 vaccination in Canada after vaccine rollout. METHODS We applied a weakly-supervised aspect-based sentiment analysis (ABSA) technique on COVID-19 vaccination-related tweets in Canada. Automatically-generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiment toward “vaccination” changed over time. In addition, we analyzed the most retweeted/liked tweets by observing most frequent nouns and sentiments toward key aspects. RESULTS After training tweets using an ABSA system, we obtained 108 aspect terms (e.g., “immunity” and “pfizer”) and 6,793 opinion terms (e.g., “trustworthy” for the positive sentiment and “jeopardize” for the negative sentiment). While manually verifying/editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for more analysis. The results showed that the top-ranked automatically-extracted aspects include “risk”, “delay”, and “hope”. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to “vaccine distribution”, “side effects”, “allergy”, “reactions” and “anti-vaxxer”, and positive sentiments related to “vaccine campaign”, “vaccine candidates”, and “immune response”. All these results indicate that the Twitter users express concerns about the safety of vaccines, but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of all the tweets, the most retweeted/liked tweets showed more positive sentiment overall, especially about vaccination itself. When looking more closely, the most retweeted/liked tweets showed an interesting dichotomy in Twitter users, i.e., the “anti-vaxxer” population who used a negative sentiment as a means to discourage vaccination and the “Covid Zero” population who used negative sentiments to encourage vaccinations while critiquing the public health response. CONCLUSIONS This study is the first to examine public sentiments toward COVID-19 vaccination on tweets over an extended period of time in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination, and get closer to the goal of ending the pandemic.

A Perspective of Electronic Cigarette Users on the COVID-19 Pandemic in Twitter: Observational Study (Preprint)

2020 ◽  
Author(s):  
Yankun Gao ◽  
Zidian Xie ◽  
Dongmei Li
Keyword(s):  
Social Media ◽  
Immune Responses ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Electronic Cigarette ◽  
Virus Infections ◽  
Twitter Data ◽  
The Us ◽  
Twitter Users ◽  
Negative Sentiment

BACKGROUND Previous studies indicated electronic cigarette users might be more vulnerable to COVID-19 infections and could develop more severe symptoms once contracted COVID-19 due to their impaired immune responses to virus infections. Social media has been widely used to express users’ responses to the COVID-19 pandemic. OBJECTIVE We aimed to examine the responses of electronic cigarette Twitter users to the COVID-19 pandemic using Twitter data. METHODS The COVID-19 dataset contained COVID-19-related Twitter posts (tweets) between March 5th, 2020 and April 3rd, 2020. Ecig group included Twitter users who didn’t have commercial accounts but ever retweeted e-cigarette promotion posts between May 2019 and August 2019. Twitter users who didn’t post or retweet any e-cigarette-related tweets were defined as Non-Ecig group. Sentiment analysis was conducted to compare sentiment scores towards the COVID-19 pandemic between both groups. Topic modeling was used to compare the main topics discussed between the two groups. RESULTS The US COVID-19 dataset consisted of 1,112,558 COVID-19-related tweets from 15,657 unique Twitter users in the Ecig group and 9,789,584 COVID-19-related tweets from 2,128,942 unique Twitter users in the Non-Ecig group. Sentiment analysis showed that the Ecig group have more negative sentiment scores than the Non-Ecig group. Results from topic modeling indicated the Ecig group had more concern about COVID-19 related death, while the Non-Ecig group cared more about the government’s responses to the COVID-19 pandemic. CONCLUSIONS Electronic cigarette Twitter users has more concern towards the COVID-19 pandemic. Twitter is a useful tool to timely monitor public responses to the COVID-19 pandemic.

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

2020 ◽  
pp. 235-242
Author(s):  
Farrikh Alzami ◽  
Erika Devi Udayanti ◽  
Dwi Puji Prabowo ◽  
Rama Aria Megantara
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Document Preparation ◽  
Learning Models ◽  
Polarity Classification ◽  
Negative Sentiment ◽  
Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

scholarly journals Optimizing Manufacturing and Osseointegration of Ti6Al4V Implants through Precision Casting and Calcium and Phosphorus Ion Implantation? In Vivo Results of a Large-Scale Animal Trial

Materials ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1670 ◽  
Author(s):  
Wölfle-Roos JV ◽  
Katmer Amet B ◽  
Fiedler J ◽  
Michels H ◽  
Kappelt G ◽  
...  
Keyword(s):  
Ion Implantation ◽  
Large Scale ◽  
In Vivo Studies ◽  
Control Group ◽  
Implant Surface ◽  
Precision Casting ◽  
Pull Out ◽  
Significant Difference ◽  
Calcium And Phosphorus

Background: Uncemented implants are still associated with several major challenges, especially with regard to their manufacturing and their osseointegration. In this study, a novel manufacturing technique—an optimized form of precision casting—and a novel surface modification to promote osseointegration—calcium and phosphorus ion implantation into the implant surface—were tested in vivo. Methods: Cylindrical Ti6Al4V implants were inserted bilaterally into the tibia of 110 rats. We compared two generations of cast Ti6Al4V implants (CAST 1st GEN, n = 22, and CAST 2nd GEN, n = 22) as well as cast 2nd GEN Ti6Al4V implants with calcium (CAST + CA, n = 22) and phosphorus (CAST + P, n = 22) ion implantation to standard machined Ti6Al4V implants (control, n = 22). After 4 and 12 weeks, maximal pull-out force and bone-to-implant contact rate (BIC) were measured and compared between all five groups. Results: There was no significant difference between all five groups after 4 weeks or 12 weeks with regard to pull-out force (p > 0.05, Kruskal Wallis test). Histomorphometric analysis showed no significant difference of BIC after 4 weeks (p > 0.05, Kruskal–Wallis test), whereas there was a trend towards a higher BIC in the CAST + P group (54.8% ± 15.2%), especially compared to the control group (38.6% ± 12.8%) after 12 weeks (p = 0.053, Kruskal–Wallis test). Conclusion: In this study, we found no indication of inferiority of Ti6Al4V implants cast with the optimized centrifugal precision casting technique of the second generation compared to standard Ti6Al4V implants. As the employed manufacturing process holds considerable economic potential, mainly due to a significantly decreased material demand per implant by casting near net-shape instead of milling away most of the starting ingot, its application in manufacturing uncemented implants seems promising. However, no significant advantages of calcium or phosphorus ion implantation could be observed in this study. Due to the promising results of ion implantation in previous in vitro and in vivo studies, further in vivo studies with different ion implantation conditions should be considered.

scholarly journals COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

2021 ◽  
pp. 1-13
Author(s):  
Usman Naseem ◽  
Imran Razzak ◽  
Matloob Khushi ◽  
Peter W. Eklund ◽  
Jinman Kim
Keyword(s):  
Sentiment Analysis ◽  
Large Scale ◽  
Data Set ◽  
Twitter Data

Cohesive Zone Model for the Interface of Multiwalled Carbon Nanotubes and Copper: Molecular Dynamics Simulation

2014 ◽  
Vol 5 (3) ◽  
Author(s):  
Ibrahim Awad ◽  
Leila Ladani
Keyword(s):  
Carbon Nanotubes ◽  
Normal Stress ◽  
Multiwalled Carbon Nanotubes ◽  
Cohesive Zone ◽  
Large Scale ◽  
Cohesive Zone Model ◽  
Dynamics Simulation ◽  
Zone Model ◽  
Multiwalled Carbon ◽  
Significant Difference

Due to their superior mechanical and electrical properties, multiwalled carbon nanotubes (MWCNTs) have the potential to be used in many nano-/micro-electronic applications, e.g., through silicon vias (TSVs), interconnects, transistors, etc. In particular, use of MWCNT bundles inside annular cylinders of copper (Cu) as TSV is proposed in this study. However, the significant difference in scale makes it difficult to evaluate the interfacial mechanical integrity. Cohesive zone models (CZM) are typically used at large scale to determine the mechanical adherence at the interface. However, at molecular level, no routine technique is available. Molecular dynamic (MD) simulations is used to determine the stresses that are required to separate MWCNTs from a copper slab and generate normal stress–displacement curves for CZM. Only van der Waals (vdW) interaction is considered for MWCNT/Cu interface. A displacement controlled loading was applied in a direction perpendicular to MWCNT's axis in different cases with different number of walls and at different temperatures and CZM is obtained for each case. Furthermore, their effect on the CZM key parameters (normal cohesive strength (σmax) and the corresponding displacement (δn) has been studied. By increasing the number of the walls of the MWCNT, σmax was found to nonlinearly decrease. Displacement at maximum stress, δn, showed a nonlinear decrease as well with increasing the number of walls. Temperature effect on the stress–displacement curves was studied. When temperature was increased beyond 1 K, no relationship was found between the maximum normal stress and temperature. Likewise, the displacement at maximum load did not show any dependency to temperature.

scholarly journals Heterogeneity and effectiveness analysis of COVID-19 prevention and control in major cities in China through time-varying reproduction number estimation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qing Cheng ◽  
Zeyi Liu ◽  
Guangquan Cheng ◽  
Jincai Huang
Keyword(s):  
Prevention And Control ◽  
Large Scale ◽  
Reproduction Number ◽  
Correlation Method ◽  
Control Measures ◽  
Time Varying ◽  
Short Period ◽  
Significant Difference ◽  
Prevention And Control Measures ◽  
And Control

AbstractBeginning on December 31, 2019, the large-scale novel coronavirus disease 2019 (COVID-19) emerged in China. Tracking and analysing the heterogeneity and effectiveness of cities’ prevention and control of the COVID-19 epidemic is essential to design and adjust epidemic prevention and control measures. The number of newly confirmed cases in 25 of China’s most-affected cities for the COVID-19 epidemic from January 11 to February 10 was collected. The heterogeneity and effectiveness of these 25 cities’ prevention and control measures for COVID-19 were analysed by using an estimated time-varying reproduction number method and a serial correlation method. The results showed that the effective reproduction number (R) in 25 cities showed a downward trend overall, but there was a significant difference in the R change trends among cities, indicating that there was heterogeneity in the spread and control of COVID-19 in cities. Moreover, the COVID-19 control in 21 of 25 cities was effective, and the risk of infection decreased because their R had dropped below 1 by February 10, 2020. In contrast, the cities of Wuhan, Tianmen, Ezhou and Enshi still had difficulty effectively controlling the COVID-19 epidemic in a short period of time because their R was greater than 1.

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