Sentiment Of Comments Research Articles

Text Analytics on YouTube Comments for Food Products

YouTube is a popular social media platform in the contemporary digital landscape. The primary focus of this study is to explore the underlying sentiment in user comments about food-related videos on YouTube, specifically within two pivotal food categories: plant-based and hedonic product. We labeled comments using sentiment lexicons such as TextBlob, VADER, and Google’s Sentiment Analysis (GSA) engine. Comment sentiment was classified using advanced Machine-Learning (ML) algorithms, namely Support Vector Machines (SVM), Multinomial Naive Bayes, Random Forest, Logistic Regression, and XGBoost. The evaluation of these models encompassed key macro average metrics, including accuracy, precision, recall, and F1 score. The results from GSA showed a high accuracy level, with SVM achieving 93% accuracy in the plant-based dataset and 96% in the hedonic dataset. In addition to sentiment analysis, we delved into user interactions within the two datasets, measuring crucial metrics, such as views, likes, comments, and engagement rate. The findings illuminate significantly higher levels of views, likes, and comments in the hedonic food dataset, but the plant-based dataset maintains a superior overall engagement rate.

Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models

Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and forum posts can provide valuable insights into user satisfaction and areas for improvement. This can guide the development of future updates and features. However, accurately identifying sentiments in software engineering datasets remains challenging. This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software engineering tasks. We conduct a comprehensive empirical study using five established datasets to assess three open-source bLLMs in zero-shot and few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs, using sLLMs to learn contextual embeddings of text from software platforms. Our experimental findings demonstrate that bLLMs exhibit state-of-the-art performance on datasets marked by limited training data and imbalanced distributions. bLLMs can also achieve excellent performance under a zero-shot setting. However, when ample training data is available or the dataset exhibits a more balanced distribution, fine-tuned sLLMs can still achieve superior results.

Analisis Sentimen Persepsi Masyarakat Terhadap Timnas Indonesia U-23 dalam AFC-23 Asian Cup 2024 Pada Media Sosial X Menggunakan Metode Naïve Bayes Classifier

Social media Twitter, or now called X, not only serves as an effective communicative medium, but it can also make it easier for people to find information or interact directly with other X users, and become a container for holding opinions. X users in Indonesia often express their opinions on all areas including sports, especially the Indonesian Football Team, which has recently been heatedly discussed because of the talent of all the players who succeeded in attracting the attention not only of football fans, but of the entire Indonesia public at the AFC ASIA CUP U 23 2024 competition held in the country of Qatar. The study uses the Naive Baiyes classification method to analyze the sentiment of comments and comments on Social Media X against each match. Sentiment data from user comments on social media X is collected and classified as positive, and negative. The final results of sentimental analysis using the naïve bayes method showed accuracy values of 73.94% out of 972 datasets, with 143 predicted negative data and 828 predictable positive data. So it can be concluded that most of the public's perception is positive because they see the struggle and performance of the Indonesian National Football Team in making the nation proud.

Comparing Product Advertising Videos: User Comments on Traditional Celebrities vs. Youtubers

Businesses are modifying their strategies to respond to the constantly changing media environment. In the past, marketing teams relied on traditional celebrities to promote brands and boost sales. Recently, social media celebrities such as Instagrammers, Tiktokers, youtubers, and influencers have emerged as popular options for marketing communication due to their significant attention and preference. Despite considerable research on traditional and social media influencer endorsements, there remains a gap in understanding how social media users perceive and interact with these endorsements. This study investigates the differences in topic, sentiment, and pragmatics of user comments on Youtube advertisement videos showcasing traditional celebrities and youtubers. We examined user comments on eight advertisement videos using content analysis, with half featuring traditional celebrities and the other half featuring youtubers. The findings indicate that including traditional celebrities in videos leads to increased user focus on brand features, along with greater expressions of admiration and positive sentiment. Furthermore, traditional celebrities are more effective than influencers in fostering favourable and reliable brand awareness. Possible explanations for these results are considered, and their theoretical and practical implications are derived.

Application of Sentiment Analysis to Explore the Connection Between Public Sentiment and Stock Price Movement by Python

This study aims to research the relationship between sentiments of public and the trend of the stock. Specially, it focuses on analyzing how positive or negative sentiment correlated to the trend of stocks. In methodology, this research investigates the predictive power of public sentiment on stock market trend by analyzing users comments on Reddit by utilizing Python Reddit API Wrapper (PRAW) to systematically web-scrape relevant financial discussion from a subreddit called stocks. After the data collecting, preprocessing step is conducted to removing noise and standardize text. Eventually, sentiment analysis is applied to analyze the sentiments of comments by using Natural Language Processing (NLP) techniques. This approach allows for an in-depth examination of the correlation between the sentiments expressed in Reddit comments and subsequent stock price fluctuations by comparing the outcome and the actual price movement. Eventually, through the comparison between the sentiment analysis result and actual stocks movement, the sentiment of public comments shares the same trend with the market movement.

TED Talks Comments Sentiment Classification Using Machine Learning Algorithms

TED Talks Comments Sentiment Classification Using Machine Learning Algorithms

Machine Learning-Based Students’ Sentiment towards E-Learning amid COVID-19 Pandemic

The global COVID-19 pandemic has created exceptional difficulties for college and university students. With educational institutions shutting down and an abrupt shift to online learning, students are facing new challenges. Despite the advantages of e-learning, developing nations like Pakistan are experiencing major hurdles due to a lack of internet access and financial constraints. As a result, students' academic performance is being negatively impacted. Researchers are using sentiment analysis of social media platforms such as Facebook, Twitter, and YouTube to gain insight into student experiences during this challenging time. This study seeks to create a machine learning-based sentiment classification system that can determine the sentiment of higher education students in Pakistan towards e-learning classes during the COVID-19 pandemic. The primary objective of this research is to identify and categorize the challenges and problems that university students face in their e-learning classes. The research employed Multi-Nominal NB and Gaussian NB classifiers to examine the sentiment of online reviews and comments of students. We achieve 99% accuracy using Gaussian NB and 98% accuracy using Multinomial NB classifiers. According to the study's results, the COVID-19 epidemic has had a significant effect on the education system, with the vast majority of feedback being negative (70%). Only a small percentage of feedback was classified as neutral (29.9%) or positive (4%). This finding underscores the urgent need for better solutions for online learning during and after the pandemic. The study underscores the importance of addressing these unique challenges faced by students in developing countries during the pandemic. There is a pressing need for innovative solutions that can ensure that higher education remains effective and accessible in these challenging times. By utilizing sentiment analysis and machine learning, researchers can gain a better understanding of the experiences of students during this crisis. This understanding can inform targeted interventions and solutions that can improve academic performance and well-being for students in developing countries like Pakistan. The findings of this research provide important insights and recommendations for policymakers, educators, and stakeholders in the higher education sector.

Leveraging spark-based machine learning algorithm for audience sentiment analysis in youtube content

In today’s digital era, the generation and sharing of information are rapidly expanding. The increased volume of complex data is big data. YouTube is the primary source of big data. The proliferation of the internet and smart devices has led to a significant increase in content creators on social media platforms, with YouTube being a prominent example. There has been a substantial increase in content creators across various social media platforms, with YouTube emerging as one of the foremost platforms for content generation and sharing. YouTubers face challenges in enhancing content strategies due to the growing number of comments, such as big data on shared videos. Reading and finding viewers’ opinions of such a large amount of data through manual methods is time-consuming and challenging and makes it hard to understand people’s sentiments. To address this, spark-based machine learning algorithms have emerged as a transformative tool for content creators to understand the audience. The Improved Novel Ensemble Method (INEM) algorithm is designed to predict viewers’ sentiments and emotional responses based on the content they interact through the comments. The proposed results provide valuable insights for content creators, helping them refine the strategies to optimize the channel’s revenue and performance. Fit Tuber Channel is analyzed to perform the sentiment of user comments.

Comparison of machine learning-based book comments sentiment analysis for constructing recommendation system

The exponential growth in the volume of books available, along with the proliferation of online platforms, has made it increasingly challenging for readers to find books tailored to their interests. This research paper aims to address this challenge by developing an effective book recommendation system based on user reviews and ratings, primarily drawn from Amazons dataset covering the period from May 1996 to July 2014. Using a K-Nearest Neighbors (KNN) algorithm and a Random Forest baseline model, the study focuses on comparative analyses in terms of Mean Squared Error (MSE) and computational costs. The KNN model outperformed the baseline model with a lower MSE of 0.15 compared to 0.38 and proved to be computationally less exacting. While the KNN model is currently the more tenable option for deployment, the paper posits that an ensemble approach may offer a more robust solution. Future work aims to include sentiment analysis, explore other recommendation algorithms, and make use of more advanced evaluation metrics. This study provides a foundation for the advancement of book recommendation systems, offering insights into their efficiency and effectiveness.

Sentiment analysis based on BiLSTM with attention mechanism on Chinese comment with stickers

As the Internet is progressively becoming larger and more intricate, more and more users of various social media choose to post their comments to express their opinions and thinking on those platforms. Analyzing the emotions contained in user comments holds great business value, helping to accurately perceive user consumption habits and improve user service levels. However, the use of emoticons and stickers in comments has increased dramatically in recent years, which brings new challenges to text sentiment analysis based on natural language processing. In this paper, in order to alleviate the above problems, we propose a method for analyzing the sentiment of Chinese comments based on the attention mechanism and BiLSTM. Specifically, we partitioned the original dataset from the Weibo platform according to the number and type of emoticons in the comments. By analyzing the actual data, the specific features of emojis that affect the performance of sentiment analysis are identified, and corresponding explanations are given. In addition, a hypothesis is proposed to quantify the impact of emoticons on model effectiveness. All the results demonstrate the effectiveness of our proposed method.

Sentimen Komentar Universitas Pelita Harapan Pada TikTok Menggunakan Metode K-Nearest Neighbor

In the evolving digital era, social media, particularly TikTok, has become a pivotal platform for information sharing and communication, including by educational institutions such as Universitas Pelita Harapan (UPH). The use of TikTok at UPH has generated diverse comments that require effective management, prompting this research to develop sentiment by using the K-Nearest Neighbor (KNN) algorithm. This study aims to address two main issues: analyzing the accuracy of the K-Nearest Neighbor algorithm in sentiment of comment sentences and measuring the performance of the K-Nearest Neighbor algorithm in calculating analysis results on comment sentences. This research employs the KNN method with a dataset of 1213 entries from 2021 to 2023 containing keywords related to UPH from TikTok platform content. The study is managed and conducted on Google Colab using the Python programming language. Based on the results of training and testing data, an accuracy of 91% is obtained, with precision at 93%, recall at 91%, and an f-1 score of 92%. From the performance of the KNN algorithm, it can be concluded that the KNN method can classify sentiment in comments

Analysis of The Sentiment of Indonesian Presidential Candidates for 2024 on The YouTube Social Media Platform using The Support Vector Machine Method

Indonesia is a country led by a president. The term of office of a president will be democratically elected every five years. The current president will end his term of office in 2024. So that in that year, the people will hold direct general elections to determine the president between 2024 and 2029. Before the general elections are held in Indonesia itself, it is closely related to the campaigns of each presidential candidate carried out by their supporters. Campaigns are usually carried out directly to village locations and social media Twitter/Facebook/YouTube. This study aims to analyze the sentiment of comments on the YouTube platform for three Indonesian presidential candidates, Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto in the context of the 2024 PresidentialElection.. Indonesia is a nation led by a president, with the presidential term lasting five years, determined through democratic elections. The incumbent president's tenure will conclude in 2024. Consequently, during that year, citizens will participate in a direct general election to select the president for the 2024- 2029 period. Prior to the domestic general elections, there is a strong link to the campaigning efforts of each presidential contender, which are conducted by their respective supporters. Campaign activities typically extend to rural areas and popular social media platforms such as Twitter, Facebook, and YouTube. This research aims to assess the sentiment of comments on the YouTube platform regarding three potential Indonesian presidential candidates: Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto. This analysis takes place in the context of the upcoming 2024 Presidential Election.

Employing the BERT model for sentiment analysis of online commentary

The objective of this research is to carry out a tone and semantic sentiment analysis of network comments on new media platforms by leveraging the BERT model. With the burgeoning popularity of social media, network comments, rich in emotional and tonal features, have emerged as a significant part of the online culture. Accurate interpretation and analysis of these comments' sentiment and semantic meanings are paramount to grasping online public opinion and user psychology. In this study, the BERT model, lauded for its bidirectional encoding and contextual understanding capabilities, is selected to scrutinize the sentiment and tone of network comments on new media platforms. Through a process of pre-training and fine-tuning, the sentiment attitudes and polarity of comments are accurately identified along with their conveyed tonal features, such as joy, anger, and sarcasm. Conducting an accurate tone and semantic sentiment analysis of network comments on new media platforms facilitates a profound understanding of user preferences and trends in public opinion. This can assist in optimizing content recommendations, enhancing user experiences, and increasing the operational effectiveness of new media platforms. The outcomes of this research will bear significant implications for studies and applications in online culture, offering invaluable references and guidance in related domains.

How Reddit Sentiments Influenced Gamestop Stocks

This paper focuses on the language usage in the GameStop (GME) share rally in January 2021. Specifically, I analyze how comments in a leading post of Reddits r/wallstreetbets subreddit reflect the reddit user communitys sentiment and emotion with various linguistic expressions. I analyzed the topics of the comments using topic modeling with Latent Dirichlet allocation algorithm, which objectively identified representative topics in the comments. Results showed that there were six clusters of topics, including two of the topic clusters that represented words that were highly correlated with stock market behaviors, such as buy and sell, one cluster that suggested objective analyses of the stock market, and one cluster that presented complaining emotions. The findings indicate a direct correlation and impact between the sentiments of comments and the performance of the stock market.

Enhanced Transformer-BLSTM Model for Classifying Sentiment of User Comments on Movies and Books

Enhanced Transformer-BLSTM Model for Classifying Sentiment of User Comments on Movies and Books

Comment Sentiment Analysis Using Bidirectional Encoder Representations from Transformers

Comment Sentiment Analysis Using Bidirectional Encoder Representations from Transformers

Online Self-Disclosure, Social Support, and User Engagement During the COVID-19 Pandemic

We investigate relationships between online self-disclosure and received social support and user engagement during the COVID-19 crisis. We crawl a total of 2,399 posts and 29,851 associated comments from the r/COVID19_support subreddit and manually extract fine-grained personal information categories and types of social support sought from each post. We develop a BERT-based ensemble classifier to automatically identify types of support offered in users’ comments. We then analyze the effect of personal information sharing and posts’ topical, lexical, and sentiment markers on the acquisition of support and five interaction measures (submission scores, the number of comments, the number of unique commenters, the length and sentiments of comments). Our findings show that: (1) users were more likely to share their age, education, and location information when seeking both informational and emotional support as opposed to pursuing either one; (2) while personal information sharing was positively correlated with receiving informational support when requested, it did not correlate with emotional support; (3) as the degree of self-disclosure increased, information support seekers obtained higher submission scores and longer comments, whereas emotional support seekers’ self-disclosure resulted in lower submission scores, fewer comments, and fewer unique commenters; and (4) post characteristics affecting audience response differed significantly based on types of support sought by post authors. These results provide empirical evidence for the varying effects of self-disclosure on acquiring desired support and user involvement online during the COVID-19 pandemic. Furthermore, this work can assist support seekers hoping to enhance and prioritize specific types of social support and user engagement .

Falsehood on social media in Egypt: Rumour detection and sentiment analysis of users’ comments

The dissemination of rumours and fabricated information via social media has the potential to adversely impact social cohesiveness and contribute to political polarization, which may lead to political divisions by casting doubt on the effectiveness of government and politicians. In light of the global economic crisis caused by the Russian–Ukrainian War, this study aims to identify economic rumours that were circulating in Egyptian society via social media. Machine learning was employed as a means of analysing the sentiment of user comments on various posts, thus providing an effective method for debunking fake news. In order to identify the most salient features of misleading information, the study qualitatively assessed the visual and linguistic elements of the postings. A total of 10,031 comments were analysed after being categorized into main groups. The study’s results revealed key features pertaining to the sentiments expressed in the comments as well as identifying common textual traits of rumours and specific visual sentiments depicted in accompanying photos. This research sheds light on the importance of identifying and debunking rumours and fabricated information in order to mitigate their potentially negative effects on social cohesiveness and political polarization. Additionally, it highlights the utility of employing machine learning as a tool for analysing sentiment in user-generated content on social media platforms.

Gender and Inconsistent Evaluations: A Mixed-methods Analysis of Feedback for Emergency Medicine Residents.

Objectives: Prior research has demonstrated that men and women emergency medicine (EM) residents receive similar numerical evaluations at the beginning of residency, but that women receive significantly lower scores than men in their final year. To better understand the emergence of this gender gap in evaluations we examined discrepancies between numerical scores and the sentiment of attached textual comments. Methods: This multicenter, longitudinal, retrospective cohort study took place at four geographically diverse academic EM training programs across the United States from July 1, 2013-July 1, 2015 using a real-time, mobile-based, direct-observation evaluation tool. We used complementary quantitative and qualitative methods to analyze 11,845 combined numerical and textual evaluations made by 151 attending physicians (94 men and 57 women) during real-time, direct observations of 202 residents (135 men and 67 women). Results: Numerical scores were more strongly positively correlated with positive sentiment of the textual comment for men (r = 0.38, P < 0.001) compared to women (r = -0.26, P < 0.04); more strongly negatively correlated with mixed (r = -0.39, P < 0.001) and negative (r = -0.46, P < 0.001) sentiment for men compared to women (r = -0.13, P < 0.28) for mixed sentiment (r = -0.22, P < 0.08) for negative; and women were around 11% more likely to receive positive comments alongside lower scores, and negative or mixed comments alongside higher scores. Additionally, on average, men received slightly more positive comments in postgraduate year (PGY)-3 than in PGY-1 and fewer mixed and negative comments, while women received fewer positive and negative comments in PGY-3 than PGY-1 and almost the same number of mixed comments. Conclusion: Women EM residents received more inconsistent evaluations than men EM residents at two levels: 1)inconsistency between numerical scores and sentiment of textual comments; and 2)inconsistency in the expected career trajectory of improvement over time. These findings reveal gender inequality in how attendings evaluate residents and suggest that attendings should be trained to provide all residents with feedback that is clear, consistent, and helpful, regardless of resident gender.

What do Twitter comments tell about news article bias? Assessing the impact of news article bias on its perception on Twitter

News stories circulating online, especially on social media platforms, are nowadays a primary source of information. Given the nature of social media, news no longer are just news, but they are embedded in the conversations of users interacting with them. This is particularly relevant for inaccurate information or even outright misinformation because user interaction has a crucial impact on whether information is uncritically disseminated or not. Biased coverage has been shown to affect personal decision-making. Still, it remains an open question whether users are aware of the biased reporting they encounter and how they react to it. The latter is particularly relevant given that user reactions help contextualize reporting for other users and can thus help mitigate but may also exacerbate the impact of biased media coverage.This paper approaches the question from a measurement point of view, examining whether reactions to news articles on Twitter can serve as bias indicators, i.e., whether how users comment on a given article relates to its actual level of bias. We first give an overview of research on media bias before discussing key concepts related to how individuals engage with online content, focusing on the sentiment (or valance) of comments and on outright hate speech. We then present the first dataset connecting reliable human-made media bias classifications of news articles with the reactions these articles received on Twitter. We call our dataset BAT - Bias And Twitter. BAT covers 2,800 (bias-rated) news articles from 255 English-speaking news outlets. Additionally, BAT includes 175,807 comments and retweets referring to the articles.Based on BAT, we conduct a multi-feature analysis to identify comment characteristics and analyze whether Twitter reactions correlate with an article’s bias. First, we fine-tune and apply two XLNet-based classifiers for hate speech detection and sentiment analysis. Second, we relate the results of the classifiers to the article bias annotations within a multi-level regression. The results show that Twitter reactions to an article indicate its bias, and vice-versa. With a regression coefficient of 0.703 (p<0.01), we specifically present evidence that Twitter reactions to biased articles are significantly more hateful. Our analysis shows that the news outlet’s individual stance reinforces the hate-bias relationship. In future work, we will extend the dataset and analysis, including additional concepts related to media bias.