User sentiment detection: a YouTube use case

In this paper we propose an unsupervised lexicon-based approach to detect the sentiment polarity of user comments in YouTube. Polarity detection in social media content is challenging not only because of the existing limitations in current sentiment dictionaries but also due to the informal linguistic styles used by users. Present dictionaries fail to capture the sentiments of community-created terms. To address the challenge we adopted a data-driven approach and prepared a social media specific list of terms and phrases expressing user sentiments and opinions. Experimental evaluation shows the combinatorial approach has greater potential. Finally, we discuss many research challenges involving social media sentiment analysis

Hybrid approach: naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments

Revolution in social media has attracted the users towards video sharing sites like YouTube. It is the most popular social media site where people view, share and interact by commenting on the videos. There are various types of videos that are shared by the users like songs, movie trailers, news, entertainment etc. Nowadays the most trending videos is the unboxing videos and in particular unboxing of mobile phones which gets more views, likes/dislikes and comments. Analyzing the comments of the mobile unboxing videos provides the opinion of the viewers towards the mobile phone. Studying the sentiment expressed in these comments show if the mobile phone is getting positive or negative feedback. A Hybrid approach combining the lexicon approach Sentiment VADER and machine learning algorithm Naive Bayes is applied on the comments to predict the sentiment. Sentiment VADER has a good impact on the Naive Bayes classifier in predicting the sentiment of the comment. The classifier achieves an accuracy of 79.78% and F1 score of 83.72%

Computer-Aided Analysis of Video Comments for Requirements Analysis

In dieser Arbeit werden Anforderungen für die Anforderungsanalyse aus den Youtube Kommentaren von vision videos extrahiert. Der Prozess der Erstellung und Vorbereitung eines Datensatzes wird beschrieben und die Güte von verschiedenen automatisierten Ansätzen wird evaluiert. Die YouTube API wird benutzt um Kommentare zu extrahieren, diese werden dann in Spam bzw. Ham kategorisiert. Die manuelle Klassifikation ist nötig um die Ergebnisse der automatischen zu verifizieren. Um Einsichten in die relevanten Kommentar zu erhalten und spezifischere Kategorien zu finden werden word clouds benutzt. Die gefundenen Kategorien sind Feature Request, Flaw Report, Safety Related, Efficiency Related und manchmal Questions. Für die automatische Klassifikation in die Kategorien Spam / Ham werden die Algorithmen Random Forest, Support Vector Machine, Linear Regression Classifier, Naive Bayes und ein Voting Classifier welcher die ersten drei kombiniert benutzt. Für die Klassifizierung in spezifische Kategorien wird ebenfalls der Voting Classifier verwendet. Für die Analyse der Stimmung werden TextBlob und SentiStrength, und um die relevanten Kommentare zusammenzufassen wird SumBasic benutzt.In this thesis requirements suitable for requirements engineering are extracted from comments below vision videos on the platform YouTube. The process of creating and preparing a dataset is described and the performance of different automated approaches is evaluated. The YouTube API is used to extract the comments, that are then classified into the categories Spam / Ham according to their content and sentiment. The manual classification is necessary to evaluate the results of the automated one. Word clouds are used to get an insight into the content of the relevant comments and decide on more specific categories to classify them according to their content. More specifically the categories Feature Request, Flaw Report, Safety Related, Efficiency Related and sometimes Questions are found. For the automated classification into the categories Spam / Ham the algorithms Random Forest, Support Vector Machine, Linear Regression Classifier, Naive Bayes, and a Voting Classifier that combines the first three are used. To classify comments according to their sentiment TextBlob and SentiStrength are used. For the classification into specific categories, the Voting Classifier is used again. The SumBasic algorithm is used to summarize the relevant comments

Lack of standards in evaluating YouTube health videos

This paper is a systematised literature review of YouTube research in health with the aim of identify the different keyword search strategies, retrieval strategies and scoring systems to assess video content. A total of 176 peer-reviewed papers about video content analysis and video evaluation were extracted from the PubMed database. Concerning keyword search strategy, 16 papers (9.09 %) reported that search terms were obtained from tools like Google Trends or other sources. In just one paper, a librarian was included in the research team. Manual retrieval is a common technique, and just four studies (2.27 %) reported using a different methodology. Manual retrieval also produces YouTube algorithm dependencies and consequently obtains biased results. Most other methodologies to analyse video content are based on written medical guidelines instead of video because a standard methodology is lacking. For several reasons, reliability cannot be verified. In addition, because studies cannot be repeated, the results cannot be verified and compared. This paper reports some guidelines to improve research on YouTube, including guidelines to avoid YouTube dependencies and scoring system issues

Mapping the ASEAN YouTube Uploaders

YouTube can now be categorized as mainstream media. It can be seen as a disruptive force in business and society, particularly concerning young people. There have been several recent studies about YouTube, providing essential insights on YouTube videos, viewers, social behavior, video traffic, and recommendation systems. However, research about YouTube uploaders has not been done much, especially YouTube uploaders from ASEAN countries. Using a combination of web content mining and content analysis, this paper reviews 600 YouTube uploaders using the data of Top 100 favorite YouTube uploaders in six ASEAN countries (Indonesia, Singapore, Malaysia, Thailand, Vietnam, and the Philippines), which are retrieved from NoxInfluencer. The study aims to provide a wider picture of YouTube uploaders' characteristics from six ASEAN countries. This study also provides useful information about how to retrieve web documents using Google Web Scrapper automatically. The study results found that the entertainment category dominated the top 100 positions of the NoxInfluencer version. In almost every country analyzed, channels related to news and politics are less attractive to YouTube users. For YouTube uploaders, YouTube can be a potential revenue source through advertising or in collaboration with specific brands. Through the analysis, we discovered that engagement is the critical factor in generating income in the form of likes, dislikes, and comments

Naïve Bayes Method for Text-Based Sentiment Analysis on Social Media

Scientometrics is the study of  measurement and analysis of science, innovation and technology through scientific publications. One form of measurement that can be taken is  the network of authors measurement. This study uses author network analysis as a measurement tool performed in scientific studies. The purpose of this study was to observe the Authorsip network formed among professors at Bina Darma University, in order to determine which professors and departments are the most productive in producing yearbook articles  or magazine. The method used in this study is the centrality of graphic degrees. Software used to view Gephi 0.9.2. The data used in this study are published data for the year 2015-2020. Based on the results of this study, it can be concluded that the agent with the highest central value is the EU with a value of 28, where the EU is the agent. with the largest number of publications. Meanwhile, the actor who has an influence or relationship and frequently collaborates on publications with the highest score on Betweenness Centrality is AM with a score of 61500.94

Metadata Extraction and Classification of YouTube Videos Using Sentiment Analysis

Soodamani, R & Varsani, V (2016), Vehicle Detection for Traffic Flow Analysis, ICCST2016, Paper presented at the IEEE International Carnahan Conference on Security Technology, 24-27 October 2016, Orlando, Florida.MPEG media have been widely adopted and is very successful in promoting interoperable services that deliver video to consumers on a range of devices. However, media consumption is going beyond the mere playback of a media asset and is geared towards a richer user experience that relies on rich metadata and content description. This paper proposes a technique for extracting and analysing metadata from a video, followed by decision making related to the video content. The system uses sentiment analysis for such a classification. It is envisaged that the system when fully developed, is to be applied to determine the existence of illicit multimedia content on the web.Final Accepted Versio

JuxtaLearn D3.2 Performance Framework

This deliverable, D3.2, for Work Package 3 incorporating the pedagogy from WP2 and orchestration factors mapped in D3.1 reviews aspects of performance in the context of participative video making. It reviews literature on curiosity and engagement characteristics of interaction mechanisms for public displays and anticipates requirements for social network analysis of relevant public videos from WP6 task 6.3. Thus, to support JuxtaLearn performance it proposes a reflective performance framework that encompasses the material environment and objects required, the participants, and the knowledge needed