scholarly journals Sentiment Analysis of Microtakaful Industry: Comparison between Indonesia and Malaysia

2019 ◽  
Vol 6 (1) ◽  
pp. 20-34 ◽  
Author(s):  
Aam Slamet Rusydiana ◽  
Irman Firmansyah ◽  
Lina Marlina
Keyword(s):  
Sentiment Analysis ◽  
Analytical Tool ◽  
Public Response ◽  
Public Sentiment ◽  
Positive Sentiment ◽  
Negative Sentiment ◽  
Industry Comparison

It is important to do research on public sentiment towards microtakaful presence in a country in order to know public response to its existence. This study aimed to determine public sentiment towards microtakaful in Indonesia and in Malaysia. Data were collected from 40 articles, journals and other writings. Data were analyzed using the software Semantria as an analytical tool in the form of text. The results showed that the assessment of existence of microtakaful in Indonesia amounted to 52% of the community showed positive sentiment, 28% indicate negative sentiment and 20% indicates a neutral sentiment. While in Malaysia that 62% showed positive sentiment, 23% negative sentiment and 15% neutral sentiment.

scholarly journals Analysis of Social Media Users Sentiments against Omnibus Law Based on Hashtags on Twitter

SISTEMASI ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 197
Author(s):  
Okta Fanny ◽  
Heri Suroyo
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Main Topic ◽  
Accuracy Score ◽  
Test Results ◽  
Bayes Classifier ◽  
The Public ◽  
Average Accuracy ◽  
Positive Sentiment ◽  
Negative Sentiment

From the research that has been done, it can be concluded that Sentiment Analysis can be used to know the sentiment of the public, especially Twitter netizens against omnibus law. After the sentiment analysis, it looks neutral artmen with the largest percentage of 55%, then positive sentiment by 35% and negative sentiment by 10%. The results of the analysis showed that the Naïve Bayes Classifier method provides classification test results with accuracy in Hashtag Pro with an average accuracy score of 92.1%, precision values with an average of 94.8% and recall values with an average of 90.7%. While Hashtag Counter For data classification, with an average accuracy value of 98.3%, precision value with an average of 97.6% and recall value with an average of 98.7%. The result of text cloud analysis conducted on a combination of hashtags both Hashtag pros and Hashtags cons, the dominant word appears is Omnibus Law which means that all hashtags in scrap is really discussing the main topic that is about Omnibus Law

scholarly journals Mining social media data to investigate patient perceptions regarding DMARD pharmacotherapy for rheumatoid arthritis

2020 ◽  
Vol 79 (11) ◽  
pp. 1432-1437 ◽  
Author(s):  
Chanakya Sharma ◽  
Samuel Whittle ◽  
Pari Delir Haghighi ◽  
Frada Burstein ◽  
Roee Sa'adon ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Social Media ◽  
Side Effects ◽  
Sentiment Analysis ◽  
Computer Algorithms ◽  
Social Media Platforms ◽  
Synthetic Agents ◽  
Positive Sentiment ◽  
Negative Sentiment ◽  
Media Data

ObjectivesWe hypothesise that patients have a positive sentiment regarding biological/targeted synthetic disease modifying anti-rheumatic drugs (b/tsDMARDs) and a negative sentiment towards conventional synthetic agents (csDMARDs). We analysed discussions on social media platforms regarding DMARDs to understand the collective sentiment expressed towards these medications.MethodsTreato analytics were used to download all available posts on social media about DMARDs in the context of rheumatoid arthritis. Strict filters ensured that user generated content was downloaded. The sentiment (positive or negative) expressed in these posts was analysed for each DMARD using sentiment analysis. We also analysed the reason(s) for this sentiment for each DMARD, looking specifically at efficacy and side effects.ResultsComputer algorithms analysed millions of social media posts and included 54 742 posts about DMARDs. We found that both classes had an overall positive sentiment. The ratio of positive to negative posts was higher for b/tsDMARDs (1.210) than for csDMARDs (1.048). Efficacy was the most commonly mentioned reason in posts with a positive sentiment and lack of efficacy was the most commonly mentioned reason for a negative sentiment. These were followed by the presence/absence of side effects in negative or positive posts, respectively.ConclusionsPublic opinion on social media is generally positive about DMARDs. Lack of efficacy followed by side effects were the most common themes in posts with a negative sentiment. There are clear reasons why a DMARD generates a positive or negative sentiment, as the sentiment analysis technology becomes more refined, targeted studies could be done to analyse these reasons and allow clinicians to tailor DMARDs to match patient needs.

scholarly journals Analyzing the Sentiment of MOOC Discussion Posts

2019 ◽  
Vol 2 (2) ◽  
pp. 1-2
Author(s):  
Haniya Ahmed ◽  
Kenny Wong
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Positive Sentiment ◽  
Negative Sentiment

The purpose of the project is to identify common difficulties that learners may face and to understand their emotions as they progress through MOOCs. MOOC is an abbreviation for the Massive Open Online Course and the research deals with the data from ten different courses from Coursera. The data is used to extract pieces of text that students have made. Then, those certain texts are required to be sent to Google Cloud Natural Language API. This app allows users to get a sentiment analysis of a text. The main goal is to assist instructors with monitoring MOOC to make it more efficient and easier for students to progress since it assists to improve the courses.  To achieve this, the first step is to gather all the data from each of the courses. Then use programming to dump all that data into one big database. The program that is used here is called Pycharm and user is required to use python and sql to aid him in dumping the data in the database. Once the database is created, coding is done to only select out the pieces of information that are needed. These texts should be where students make comments or ask questions. Next, the data is queried to send these texts to Google Cloud Natural Language API. Here, the program breaks down all the sentences to only be just words. Then the program is going to categorize each word according to whether its connotation is positive, negative or neutral. Next, all the words are sorted according to their connotations. The overall sentiment depends on the emotion that has the highest number. If positives and negatives are all balanced out then the sentiment is neutral. Sentiment scores range from -1 to 1, where -1 is the most negative, 1 is the most positive and anywhere near 0 is neutral.  Positive sentiment scores indicate instructors that students are doing well on their course and neutral sentiment scores indicate that the course is balanced out with difficulties and easy tasks. However, negative sentiment is the most important to instructors since it indicates them that students are struggling and they need to improve the course.

scholarly journals Public Sentiment Analysis of Online Transportation in Indonesia through Social Media Using Google Machine Learning

2021 ◽  
pp. 153
Author(s):  
Desak Ayu Savita ◽  
I Ketut Gede Darma Putra ◽  
Ni Kadek Dwi Rusjayanthi
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Level Of Service ◽  
Public Sentiment ◽  
Analysis Process ◽  
Transportation Sector ◽  
Negative Sentiment

Public opinion is important to agencies or parties in particular fields, as it may indicate a tendency of public's view towards something (such as an object or process). One of them is in the transportation sector. Transportation has become a necessity for the community, many things more effective and efficient online, so that online transportation becomes important for society. The proliferation of online transportation, caused citizens to express opinions through social media. It is important to know the level of service of online transportation considering the large number of users, so that it can be used as a basis for improvement. One of the methods public opinion in social media is by sentiment analysis. The study used the help of Google Machine Learning for the sentiment analysis process that can produce 82,6% of accuracy number, 82,2% of precision, 83,3% of recall with the most sentiment result indicate to public opinion falls into the negative sentiment category for Gojek companies in media social of Twitter.

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.

scholarly journals Changes in public response associated with various COVID-19 restrictions in Ontario, Canada: an observational study using social media time series data (Preprint)

2021 ◽  
Author(s):  
Antony Chum ◽  
Andrew Nielsen ◽  
Zachary Bellows ◽  
Eddie Farrell ◽  
Pierre-Nicolas Durette ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Public Opinion ◽  
News Media ◽  
School Closure ◽  
Social Distancing ◽  
Public Response ◽  
Collective Attention ◽  
Positive Sentiment ◽  
Negative Sentiment

BACKGROUND News media coverage of anti-mask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views, but does little to represent views of the general public. Investigating the public’s response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policymakers to craft better public health messages in anticipation of poor reactions to controversial restrictions. OBJECTIVE Using data from social media, this study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (e.g. business and school closure, regional lockdown differences, additional public health restrictions such as social distancing and masking). METHODS COVID-related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 to Oct 31 2020. Sentiment scores were calculated using the VADER algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites, and dynamic regression models with ARIMA errors were used to examine the association between public health restrictions and changes in public opinion over time (i.e. collective attention, aggregate positive sentiment, and level of disagreement) controlling for the effects of confounders (i.e. daily COVID-19 case counts, holidays, COVID-related official updates). RESULTS In addition to expected direct effects (e.g. business closure led to decreased positive sentiment and increased disagreements), the impact of restriction on public opinion is contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closure and other restrictions (e.g. masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (i.e. sentiment polarization). Partial (region-targeted) lockdowns were associated with better public response (i.e. higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns. CONCLUSIONS Our study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policymakers anticipate public response to future pandemic restrictions, and ensure adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed, but controversial, restrictions.

scholarly journals ANALYSIS OF WARGANET COMMENTS ON IT SERVICES IN MANDIRI BANK USING K-NEAREST NEIGHBOR (K-NN) ALGORITHM BASED ON ITSM CRITERIA

2019 ◽  
Vol 1 (1) ◽  
pp. 14-19
Author(s):  
Febrian Wahyu Ramadhan ◽  
Husni Teja Sukmana ◽  
Lee Kyung Oh ◽  
Luh Kesuma Wardhani
Keyword(s):  
Sentiment Analysis ◽  
Test Data ◽  
Nearest Neighbor ◽  
Training Data ◽  
It Services ◽  
K Nearest Neighbor ◽  
Data Indexing ◽  
Positive Sentiment ◽  
Negative Sentiment ◽  
F Measure

Sentiment analysis is a method for reviewing products or services to determine opinions or feelings about a product. The results of the analysis can be used by companies as evaluation materials and considerations to improve the products or services provided. This study aims to test the level of public sentiment on the quality of Bank Mandiri services that have received ISO 20000-1 with the application of sentiment analysis using the K-NN algorithm based on ITSM criteria. The initial classification in this study uses the lexicon method by detecting words included in sentiment words, the results of which are included as labels on training data and test data. Formation of the classification with the K-NN algorithm by taking into account the results of the training data indexing and weighting of the test data, with the value of k as the decision maker limit. The trial results of 10 scenarios show that the classification using the K-NN algorithm as a sentiment classification is 98% accuracy value of 50 test data to 600 training data, with 24% getting positive sentiment, 22% negative sentiment and 55% neutral sentiment, with f -measure 95.83%. while in testing 100 the test data obtained 79% accuracy value with 21% getting positive sentiment, 42% negative sentiment and 38% neutral with an f-measure value of 68.42%.

scholarly journals Text Mining Approaches to Analyze Public Sentiment Changes Regarding COVID-19 Vaccines on Social Media in Korea

2021 ◽  
Vol 18 (12) ◽  
pp. 6549
Author(s):  
Jae-Geum Shim ◽  
Kyoung-Ho Ryu ◽  
Sung Hyun Lee ◽  
Eun-Ah Cho ◽  
Yoon Ju Lee ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Time Course ◽  
Latent Dirichlet Allocation ◽  
Allocation Model ◽  
Public Response ◽  
Latent Dirichlet Allocation Model ◽  
Public Sentiment ◽  
Before And After

The COVID-19 pandemic has affected the entire world, resulting in a tremendous change to people’s lifestyles. We investigated the Korean public response to COVID-19 vaccines on social media from 23 February 2021 to 22 March 2021. We collected tweets related to COVID-19 vaccines using the Korean words for “coronavirus” and “vaccines” as keywords. A topic analysis was performed to interpret and classify the tweets, and a sentiment analysis was conducted to analyze public emotions displayed within the retrieved tweets. Out of a total of 13,414 tweets, 3509 were analyzed after preprocessing. Eight topics were extracted using the Latent Dirichlet Allocation model, and the most frequently tweeted topic was vaccine hesitation, consisting of fear, flu, safety of vaccination, time course, and degree of symptoms. The sentiment analysis revealed a similar ratio of positive and negative tweets immediately before and after the commencement of vaccinations, but negative tweets were prominent after the increase in the number of confirmed COVID-19 cases. The public’s anticipation, disappointment, and fear regarding vaccinations are considered to be reflected in the tweets. However, long-term trend analysis will be needed in the future.

ANALYSIS OF WARGANET COMMENTS ON IT SERVICES IN MANDIRI BANK USING K-NEAREST NEIGHBOR (K-NN) ALGORITHM BASED ON ITSM CRITERIA

2019 ◽  
Vol 1 (1) ◽  
pp. 14-19
Author(s):  
Febrian Wahyu Ramadhan ◽  
Husni Teja Sukmana ◽  
Lee Kyung Oh ◽  
Luh Kesuma Wardhani
Keyword(s):  
Sentiment Analysis ◽  
Test Data ◽  
Nearest Neighbor ◽  
Training Data ◽  
It Services ◽  
K Nearest Neighbor ◽  
Data Indexing ◽  
Positive Sentiment ◽  
Negative Sentiment ◽  
F Measure

Sentiment analysis is a method for reviewing products or services to determine opinions or feelings about a product. The results of the analysis can be used by companies as evaluation materials and considerations to improve the products or services provided. This study aims to test the level of public sentiment on the quality of Bank Mandiri services that have received ISO 20000-1 with the application of sentiment analysis using the K-NN algorithm based on ITSM criteria. The initial classification in this study uses the lexicon method by detecting words included in sentiment words, the results of which are included as labels on training data and test data. Formation of the classification with the K-NN algorithm by taking into account the results of the training data indexing and weighting of the test data, with the value of k as the decision maker limit. The trial results of 10 scenarios show that the classification using the K-NN algorithm as a sentiment classification is 98% accuracy value of 50 test data to 600 training data, with 24% getting positive sentiment, 22% negative sentiment and 55% neutral sentiment, with f -measure 95.83%. while in testing 100 the test data obtained 79% accuracy value with 21% getting positive sentiment, 42% negative sentiment and 38% neutral with an f-measure value of 68.42%.

scholarly journals Lexicon Based Sentiment Analysis in Indonesia Languages : A Systematic Literature Review

2021 ◽  
Vol 1 (1) ◽  
pp. 363-367
Author(s):  
Yuli Fauziah ◽  
Bambang Yuwono ◽  
Agus Sasmito Aribowo
Keyword(s):  
Literature Review ◽  
Sentiment Analysis ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Main Question ◽  
Maximum Accuracy ◽  
Stop Word ◽  
Positive Sentiment ◽  
Negative Sentiment

This systematic literature review aims to determine the trend of lexicon based sentiment analysis research in Indonesian Language in the last two years. The focus of the study is on the understanding of preprocessing used in lexicon-based sentiment analysis studies in the last two years, the lexicon used in these studies, and classification accuracy. The main question in this SLR : what techniques of lexicon based sentiment analysis will provide the highest accuracy. The most widely used preprocessing methods in previous research are tokenization, case conversion, stemming, remove punctuation, remove stop word, remove or replace emoji and emoticons, and normalization or slangword conversion. The sentiment labeling process in previous studies calculated based on the comparison of the number of negative sentiment keywords with positive sentiment keywords in one sentence. The maximum accuracy from previous study is 90%. The most widely used lexicon is NRC and Inset which is a lexicon dictionary in Indonesian. Knowledge of this can be used to propose a better model for lexicon based sentiment analysis in Indonesian Languages.

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