Examining Digital Activism through Sentiments for Feminism Issues on Instagram (Case Study @perempuanberkisah)

The AFF Cup is a football competition organized by the ASEAN Football Federation, or AFF for short. The 2020 AFF Cup was held in 2021 due to the COVID-19 pandemic. The Indonesian National Team advanced to the final round and became runner-up in the championship. With the end of the championship and the Indonesian National Team having to accept defeat in the final, the public responded through tweets on Twitter. Through these tweets, it will be known how the public evaluates the performance of the Indonesian National Team in the 2020 AFF Cup. It is vital to carry out this research to obtain information regarding society's response. The research that will be conducted is sentiment analysis. Sentiment analysis will be carried out on Rapid Miner software, with the algorithms used being Naïve Bayes and K-Nearest Neighbor. The data used to perform sentiment analysis are tweets from Twitter taken using SNScrape. This research aims to analyze public responses to the Indonesian National Team in the 2020 AFF Cup. This research will determine the percentage of positive, neutral, and negative sentiments from public responses. So that later it can be concluded how the public responds to the Indonesian National Team, whether positive, neutral, or negative. It is also to find out which algorithm has the higher accuracy. The results obtained for Naive Bayes with an accuracy of 64.74% are 71.54% positive sentiment, 15.45% neutral sentiment, and 13.01% negative sentiment. For K-Nearest Neighbor, with an accuracy of 65.64% is 80.49% positive sentiment, 15.45% neutral sentiment, and 4.06% negative sentiment. Both algorithms have the highest accuracy compared to other algorithms in Rapid Miner when the sentiment analysis is performed, with K-Nearest Neighbor having slightly higher accuracy. Most tweets about the Indonesian National Team in the 2020 AFF Cup had positive sentiments. Based on these results, it can be concluded that even though the Indonesian National Team did not win the 2020 AFF Cup, the public still responded positively.

How does the stock market react to the media coverage about a firm’s efforts in addressing non-financial stakeholder claims during COVID-19 pandemic? Focusing on daily data in the first quarter of 2020, I have examined the relationship between daily abnormal returns (AR) of US listed companies and independent media sentiment on a firm’s issues related to customers, employees, community, and the environment. Merging Compustat security daily data and TruValue Labs daily sentiment data, I constructed a panel data of 313,049 firm-day observations, representing 5,133 listed firms in the US. I first measured sentiment for each stakeholder’s issues as four dummies: no news, negative sentiment, neutral sentiment, and positive sentiment. Random-effect panel regressions report that, compared to no news or neutral sentiment, negative and positive sentiment on customer issues may result in an additional daily AR of 13.207 p.p. (p<0.001) and 14.316 p.p. (p<0.001) respectively. On employee issues, negative sentiment would result in a lower daily AR by 6.394 p.p. (p<0.05), compared to no news, neutral or positive sentiment – the three of which appear to have no statistically significantly different effects on daily AR. On community issues, whether there is news or whether the news was in a negative, neutral, or positive sentiment would not have statistically significantly different effects on AR. On environment issues, positive and negative sentiment would cause a lower daily AR by 8.724 p.p. (p<0.01) and 7.539 p.p. (p<0.01) respectively, compared to no news or neutral sentiment. Finally, I measured sentiment as a continuous value between -1 (most negative) and 1 (most positive), and found that only employee sentiment has statistically significant linear effects on AR. Daily AR would increase by 7.325 p.p. (p<0.1) and 9.277 p.p. (p<0.05) respectively if the sentiment on employee issues increased by 1, under the assumptions of “no news is missing information” and “no news is neutral news”.

Purpose There is a strong need for companies to monitor customer-generated content of social media, not only about themselves but also about competitors, to deal with competition and to assess competitive environment of the business. The purpose of this paper is to help companies with social media competitive analysis and transformation of social media data into knowledge creation for decision-makers, specifically for app-based food delivery companies. Design/methodology/approach Three online app-based food delivery companies, i.e. Swiggy, Zomato and UberEats, were considered in this study. Twitter was used as the data collection platform where customer’s tweets related to all three companies are fetched using R-Studio and Lexicon-based sentiment analysis method is applied on the tweets fetched for the companies. A descriptive analytical method is used to compute the score of different sentiments. A negative and positive sentiment word list is created to match the word present on the tweets and based on the matching positive, negative and neutral sentiments score are decided. The sentiment analysis is a best method to analyze consumer’s text sentiment. Lexicon-based sentiment classification is always preferable than machine learning or other model because it gives flexibility to make your own sentiment dictionary to classify emotions. To perform tweets sentiment analysis, lexicon-based classification method and text mining were performed on R-Studio platform. Findings Results suggest that Zomato (26% positive sentiments) has received more positive sentiments as compared to the other two companies (25% positive sentiments for Swiggy and 24% positive sentiments for UberEats). Negative sentiments for the Zomato was also low (12% negative sentiments) compared to Swiggy and UberEats (13% negative sentiments for both). Further, based on negative sentiments concerning all the three food delivery companies, tweets were analyzed and recommendations for business provided. Research limitations/implications The results of this study reveal the value of social media competitive analysis and show the power of text mining and sentiment analysis in extracting business value and competitive advantage. Suggestions, business and research implications are also provided to help companies in developing a social media competitive analysis strategy. Originality/value Twitter analysis of food-based companies has been performed.

The waste problem is a severe problem that significantly affects the environment and public health. To effectively determine the public’s perception of the waste problem, it is necessary to examine public sentiment toward waste management. This research aims to develop a sentiment analysis model using VADER and deep-translator and analyze the Yogyakarta waste emergency problem. This research was conducted in two phases, namely, the first phase was developing a sentiment analysis model by evaluating its performance based on public data. Then, the second phase classifies public comments from YouTube regarding the waste problem to understand public perceptions and evaluations by identifying positive, negative, and neutral sentiments. The model evaluation results show that sentiment analysis using VADER and deep translator can achieve Accuracy, Precision, Recall, and F1-score values of 0.716, 0.837, 0.853, and 0.738, respectively. The sentiment results from YouTube comments obtained positive, neutral, and negative sentiments of 30.0%, 31.7%, and 37.3%, respectively. The results of the sentiment analysis are neutral sentiment discussing waste management, disappointment in negative sentiment, and hope for waste management in positive sentiment.

PurposeThis study quantified companies' views on the COVID-19 pandemic with sentiment analysis of US public companies' disclosures. The study aims to provide timely insights to shareholders, investors and consumers by exploring sentiment trends and changes in the industry and the relationship with stock price indices.Design/methodology/approachFrom more than 50,000 Form 10-K and Form 10-Q published between 2020 and 2021, over one million texts related to the COVID-19 pandemic were extracted. Applying the FinBERT fine-tuned for this study, the texts were classified into positive, negative and neutral sentiments. The correlations between sentiment trends, differences in sentiment distribution by industry and stock price indices were investigated by statistically testing the changes and distribution of quantified sentiments.FindingsFirst, there were quantitative changes in texts related to the COVID-19 pandemic in the US companies' disclosures. In addition, the changes in the trend of positive and negative sentiments were found. Second, industry patterns of positive and negative sentiment changes were similar, but no similarities were found in neutral sentiments. Third, in analyzing the relationship between the representative US stock indices and the sentiment trends, the results indicated a positive relationship with positive sentiments and a negative relationship with negative sentiments.Originality/valuePerforming sentiment analysis on formal documents like Securities and Exchange Commission (SEC) filings, this study was differentiated from previous studies by revealing the quantitative changes of sentiment implied in the documents and the trend over time. Moreover, an appropriate data preprocessing procedure and analysis method were presented for the time-series analysis of the SEC filings.

This study aims to test the effectiveness of the Policy implemented by the Indonesian government to increase national tax revenues through tax incentive programs and solutions to negative sentiment and see the progress of research on “Tax Incentives in Indonesia” around the world published by journals on that theme. This research uses qualitative methods with bibliometric analysis and sentiment analysis approaches. The data used is secondary data with the theme “Tax Incentive Indonesia” from the Dimension database of 80 published journals. Then, the data is processed and analyzed using the VOSviewer application to know the bibliometric map of research development “Tax Incentive Indonesia” in this world. Apart from that, the data was also analyzed using the SentiStrength application to find out positive sentiment, negative, and neutral sentiment regarding the tax incentive policy that has been implemented in Indonesia. The results of the research found that the tax incentive program has proven effective in increasing national tax revenues and the solution to negative sentiment is to create activities that can increase taxpayers’ awareness of paying taxes, and trust in the government as well as direct benefit programs for taxes that have been paid. Then based on sentiment analysis, neutral sentiment is the highest result with a percentage of 50%, followed by positive and negative sentiment with each having a percentage of 25%. Meanwhile, based on bibliometric keyword mapping, 5 clusters can become research paths.

Introduction Forms of sexual dysfunction remain some of the most common concerns amongst testicular cancer survivors. While it is commonly reported, discussions surrounding male sexual dysfunction among patients with testicular cancer in public social media forums are understudied. Objective We sought to evaluate themes and tones on Reddit, a large social media platform, regarding men’s health conditions in patients with testicular cancer. Methods We retrospectively analyzed discussion posts on Reddit using qualitative analysis and natural language processing-based sentiment analysis. Using the Reddit application programming interface (API), 1000 posts from April to June 2023 were extracted from the subreddit r/testicularcancer, which is dedicated to discussions related to testicular cancer and has &amp;gt;5,900 active members. Men’s health issues were defined to include erectile dysfunction, low libido, testicular discomfort, or ejaculatory dysfunction (e.g., delayed or premature ejaculation, anejaculation, painful ejaculation). Qualitative analysis was performed to determine overall themes within discussion posts. Sentiment analysis was performed using the validated tool VADER, which is specifically attuned to sentiments expressed on social media platforms and assigns a score for each post that can be interpreted as having a positive (score of 1), neutral (score of 0), or negative (score of -1) sentiment. Results A total of 148 posts were randomly selected for thematic and sentiment analysis. Notable discussion themes from the posts include discussing treatment options (e.g., chemotherapy vs. surgery vs. observation) offered by the physician (24.3%); asking about side effects of treatment (17.6%); discussing a patient’s personal journey with testicular cancer (33.1%); asking for advice from the online community (68.9%); and discussing various emotions (frustration, anxiety, stress, etc.) (28.4%). Fifteen (10.1%) posts mentioned a men’s health issue within the text body. Of these 15 posts, 7 discussed low libido, 4 discussed testicular pain or discomfort, 3 highlighted ejaculatory dysfunction, and 1 highlighted a physiologic effect of low testosterone levels. The mean (SD) VADER score was 0.1 (0.9); most posts (75; 51.0%) had a positive sentiment while 62 (42.2%) had a negative sentiment. Posts discussing men’s health issues did not have a significantly stronger positive or negative tone than other posts (p = 0.83). Conclusions On Reddit, the slim majority of posts had positive sentiment and patients seemed to prioritize discussing their journey with testicular cancer and its various treatment options along with asking for community advice and second opinions. Upon initial analysis, a smaller but notable percentage of posts mentioned a men’s health issue, indicating that men’s health issues may be underdiscussed on social media venues despite its prevalence in this particularly vulnerable patient population. A variety of reasons may explain this, including stigma involving discussing sexual dysfunction in public forums and a lack of prioritization and focus on men’s health issues in the context of living with cancer. Disclosure No.

IntroductionHuman behavior is significantly influenced by emotions, with negative sentiments such as fear and anxiety driving various coping mechanisms, including cognitive behavioral therapy (CBT), dietary changes, and medication use. Social media platforms like X (formerly Twitter) offer valuable insights into these behaviors due to their real-time, user-generated content. While previous research has explored general sentiment on X (formerly Twitter), there has been limited focus on the reasons behind negative sentiments and the coping strategies employed, particularly in relation to brain health.MethodsWe analyzed 390,000 X-posts tagged with #brain and #health, categorizing them into positive, negative, and neutral sentiments. We then investigate the use of CBT techniques, dietary adjustments, and specific medications across these sentiments.ResultsOur findings reveal distinct patterns in how negative and positive sentiments are expressed and managed on social media. Negative sentiments are often linked to serious health concerns, such as COVID-19 and brain inflammation, and exhibit various cognitive distortions. These X-posts also mention coping strategies like using medications such as lorazepam and simvastatin, or consuming comfort foods like pizza. In contrast, positive sentiments emphasize resilience and improvement, with mentions of mindfulness, supplements, and medications like doxycycline and pregabalin. The study also highlights the risk of disseminating information about dietary and drug supplements that may not be suitable for public use due to serious side effects, such as Chaga mushrooms, which, despite being associated with positive sentiment, are known to cause renal failure in certain cases.ConclusionOverall, the study profiles the use of positive and negative brain health sentiment of X, which underscores both the advantages and risks of using X (formerly Twitter) as a platform for sharing brain health-related information.

The presence and significance of artificial intelligence (AI) technology in society have been steadily increasing since 2000. While its potential benefits are widely acknowledged, concerns about its impact on society, the economy, and ethics have also been raised. Consequently, artificial intelligence has garnered widespread attention in news media and popular culture. As mass media plays a pivotal role in shaping public perception, it is crucial to evaluate opinions expressed in these outlets. Understanding the public’s perception of artificial intelligence is essential for effective public policy and decision making. This paper presents the results of a sentiment analysis study conducted on WIRED magazine’s coverage of artificial intelligence between January 2018 and April 2023. The objective of the study is to assess the prevailing opinions towards artificial intelligence in articles from WIRED magazine, which is widely recognized as one of the most reputable and influential publications in the field of technology and innovation. Using two sentiment analysis techniques, AFINN and VADER, a total of 4265 articles were analyzed for positive, negative, and neutral sentiments. Additionally, a term frequency analysis was conducted to categorize articles based on the frequency of mentions of artificial intelligence. Finally, a linear regression analysis of the mean positive and negative sentiments was performed to examine trends for each month over a five-year period. The results revealed a leading pattern: there was a predominant positive sentiment with an upward trend in both positive and negative sentiments. This polarization of sentiment suggests a shift towards more extreme positions, which should influence public policy and decision making in the near future.

Sentiment analysis is one part of natural language processing. Sentiment analysis can be done by lexicon based, or machine learning based. Sentiment analysis based on machine learning has advantage of dynamism to meet with new language datasets or new vocabulary. Sentiment analysis seeks to understand the sentiments contained in a sentence. A sentence can be positive, neutral or negative, based on its sentiments. A sentence can have positive, neutral or negative sentiments. However, the fact is each sentence does not always have positive, negative or neutral sentiment clearly. We try to develop a sentiment analysis method that can show the sentiment degree of a sentence. Fuzzy sentiment analysis using convolutional neural network are introduced in this paper to produce more accurate sentiment analysis results. Convolutional neural networks are a popular machine learning method for sentiment analysis. The concept of fuzzy sets is used to express the sentiment degree of a sentence. Euclidean distance analysis to determine the proximity of two vectors is used to show that this method is better than the standard method. The method we propose successfully produces a value that indicates the degree of sentiment of a sentence. Comparison of the euclid distance between the results of the standard sentiment analysis and our method shows that the results of the fuzzy sentiment analysis using convolutional neural network have a distance that is relatively close to the true sentiment value. Fuzzy convolutional neural network analysis sentiment is proven to be able to produce better and smoother sentiment analysis results than standard methods.

This paper presents multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between 25 May 2020 and 31 January 2023. First, the analysis shows that the average number of Tweets per month wherein individuals self-reported Long COVID on Twitter was considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings from sentiment analysis using VADER show that the percentages of Tweets with positive, negative, and neutral sentiments were 43.1%, 42.7%, and 14.2%, respectively. To add to this, most of the Tweets with a positive sentiment, as well as most of the Tweets with a negative sentiment, were not highly polarized. Third, the result of tokenization indicates that the tweeting patterns (in terms of the number of tokens used) were similar for the positive and negative Tweets. Analysis of these results also shows that there was no direct relationship between the number of tokens used and the intensity of the sentiment expressed in these Tweets. Finally, a granular analysis of the sentiments showed that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.

It is aimed to identify the basic success factors, which are essential for startups as they intend to develop successful and profitable business models over time. To this end, it is attempted to analyze the sentiments on user-generated content (UGC) on Twitter. First, trigram word cloud is used. Then, a sentiment analysis is done with various predictive models including random forest, support-vector machine (SVM) and multilayer perceptron (MLP) to test the labeling of unlabeled data. To divide topics into negative, positive, and neutral sentiments, latent Dirichlet allocation (LDA) has been applied. According to the results, the MLP method on the basis of accuracy criterion offers an accuracy of 0.81, which is higher than other tested methods. In this regard, random forest and SVM methods provide accuracy of 0.78 and 0.80, respectively. Voting and stacking algorithms were used to increase the accuracy of the algorithms. However, it is found that with the use of voting method, the accuracy is almost equal to the results obtained from the MLP and with stacking method the accuracy is less than all three methods. Using word cloud, it is indicated that the most negative trigram is startups innovation regarding climate change, the most positive one is product marketing management and business-related concepts are determined as neutral. It is found that startup acceleration process, pushing for quicker completion, delivering the best product at the beginning of the project, poor management practices, and focusing just on properties are grouped as negative sentiments. On the other hand, sustainable and innovative business plan, the presence of experienced entrepreneurs and investors, coronavirus (COVID-19), and innovation are recognized as positive sentiments, and no analysis is given for neutral sentiments.

This study compares the effectiveness of the Support Vector Machine (SVM) and Naïve Bayes algorithms in classifying user sentiment regarding the BRImo application. User reviews were obtained from the Google Play Store platform and underwent a text preprocessing stage to clean and prepare the data. Subsequently, the SVM and Naïve Bayes algorithms were applied for sentiment analysis, using evaluation metrics such as accuracy, precision, recall, and F1-score. The results show that SVM achieved a training accuracy of 95.67% and a testing accuracy of 83.11%, with its best performance on positive sentiment (precision 92.26%, recall 91.79%, F1-score 92.02%) and moderate performance on negative sentiment (precision 62.81%, recall 62.81%, F1-score 62.81%). Meanwhile, Naïve Bayes recorded a training accuracy of 95.23% and a testing accuracy of 82.77%, with its highest performance on positive sentiment (precision 90.12%, recall 93.38%, F1-score 91.72%) but lower performance on negative sentiment (precision 65.07%, recall 60.06%, F1-score 62.46%). In terms of sentiment distribution, SVM was more effective in handling sentiment variations, particularly in detecting negative and neutral sentiments. These findings indicate that SVM outperforms Naïve Bayes in sentiment analysis of user reviews for the BRImo application.

Twitter is a popular social media platform where the public is free to comment and write about anything. It is common for people to post comments containing harsh words and even hate speech. The 2019 presidential election in Indonesia generated a significant amount of comments, with some users praising the candidates, others criticizing them, and some even resorting to insults. To extract meaningful information from these comments and classify the text, sentiment analysis is essential. In this research, sentiment analysis involves the process of categorizing textual documents into two classes: negative and positive sentiment. The opinion data was collected from the Twitter social network in the form of tweets related to the 2019 presidential election. The dataset used in the study consisted of 3,337 tweets, which were divided into 70% training data and 30% test data. The training data comprised tweets whose sentiment was already known, serving as a foundation for the model to learn and make predictions. The primary objective of this research is to determine whether the tweets, written in Indonesian, express positive or negative sentiments. The Naive Bayes Classifier algorithm was employed to classify the tweet data. This algorithm is well-suited for text classification tasks due to its simplicity and efficiency in handling large datasets. The classification results on the test data demonstrated that the Naive Bayes Classifier algorithm achieved an overall accuracy of 71%. Specifically, the accuracy for negativesentiment classification was 71%, while the accuracy for positive sentiment classification was 70%. These results indicate that the Naive Bayes Classifier is effective in distinguishing between positive and negative sentiments in tweets related to the presidential election

An account with the name Bjorka claims to have obtained billions of SIM card registration data in the form of Identity Card and Family Card Nuber from the government database of the Ministry of Communication and Informatics (Kemkominfo), people start questioning the cybersecurity of the government database. The appearance of the Bjorka hacker caused various responses on Twitter, some supported Bjorka’s action and some disagree. Hence the need for sentiment analysis to determine public sentiment is more towards negative or positive, so the government can do evaluation as well as strategic planning to deal with future data leaking incidents. This study uses tweets that contain public responses to predict negative or positive sentiment using Support Vector Machine algorithm. From a total of 1017 public response data, have been found 97.35% (990 tweets) to have negative sentiment and 2.65% (27 tweets) have positive sentiment, so it can be known that public responses are towards negative about data leaking by Bjorka. In conclusion, education to the public about data leaks by Bjorka is not the main priority to do for the government. The government can focus more on dealing with other sectors such as improving the security of the data itself.