scholarly journals Let’s play on Facebook: using sentiment analysis and social media metrics to measure the success of YouTube gamers’ post types

2019 ◽  
Author(s):  
Flora Poecze ◽  
Claus Ebster ◽  
Christine Strauss
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Nearest Neighbor ◽  
Consumer Feedback ◽  
Youtube Videos ◽  
The Masses ◽  
Processing Techniques ◽  
Facebook Pages ◽  
Future Work

AbstractThis paper discusses the analysis results of successful self-marketing techniques on Facebook pages in the cases of three YouTube gamers: PewDiePie, Markiplier, and Kwebbelkop. The research focus was to identify significant differences in terms of the gamers’ user-generated Facebook metrics and commentary sentiments. Analysis of variance (ANOVA) and k-nearest neighbor sentiment analysis were employed as core research methods. ANOVA of the classified post categories revealed that photos tended to show significantly more user-generated interactions than other post types, while, on the other hand, re-posted YouTube videos gained significantly fewer numbers in the retrieved metrics than other content types. K-nearest neighbor sentiment analysis pointed out underlying follower negativity in cases where user-generated activity was relatively low, thereby improving the understanding of the opinion of the masses previously hidden behind metrics such as the number of likes, comments, and shares. The paper at hand highlights the methodological design of the study as well as a detailed discussion of key findings and their implications, and future work. The results per se indicate the need to utilize natural language processing techniques to optimize brand communication on social media and highlight the importance of considering machine learning sentiment analysis techniques for a better understanding of consumer feedback.

scholarly journals Sentiment analysis of social media content in Croatian health industry

2021 ◽  
Vol 9 (1) ◽  
pp. 37-57
Author(s):  
Jelena Mušanović ◽  
Jelena Dorčić ◽  
Tea Baldigara
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Modern Society ◽  
Essential Elements ◽  
Point Of View ◽  
Daily Routine ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality ◽  
Facebook Pages

While social media have become a daily routine in modern society, brand communication and engagement with customers have become essential elements of marketing strategy and success in the tourism and hotel industry. This revolution of social media, in tourism and hospitality marketing, contributed to the rise of a novel sentiment analysis from a machine learning and natural language processing point of view. The purpose of the study is: to provide a general descriptive overview of comments posted by Facebook page followers; to identify specific textual attributes of hotel brand posts on social media and to apply the sentiment analysis to Facebook comments from four- and five-star hotel brands in Croatia to identify and compare customers’ feelings and attitudes towards the staff, services and products that hotel brands promote by posting messages on Facebook pages. To analyse hotel brand sentiments, the authors collected a total of 4,248 comments and 2,373 postings in English, German and Italian. The results showed that the comments on four- and five-star hotel brands expressed predominantly positive sentiments. Despite the positively oriented sentiments in the comments, Facebook page followers are predominantly passive users and do not tend to comment actively. The results can be used by marketers in the tourism and hospitality industry to plan their future social media communication strategies.

scholarly journals An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

2020 ◽  
Author(s):  
Sohini Sengupta ◽  
Sareeta Mugde ◽  
Garima Sharma
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Gold Mine ◽  
Social Media Platforms ◽  
Processing Techniques

Twitter is one of the world's biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

scholarly journals Análise de discursos em notícias sobre homofobia, racismo e sexismo em comentários de portais brasileiros de notícias

2021 ◽  
Author(s):  
Lucas Rodrigues ◽  
Antonio Jacob Junior ◽  
Fábio Lobato
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Data Visualization ◽  
Language Processing ◽  
Topic Modeling ◽  
Hate Speech ◽  
Psychological Impact ◽  
Internet Service ◽  
General Law

Posts with defamatory content or hate speech are constantly foundon social media. The results for readers are numerous, not restrictedonly to the psychological impact, but also to the growth of thissocial phenomenon. With the General Law on the Protection ofPersonal Data and the Marco Civil da Internet, service providersbecame responsible for the content in their platforms. Consideringthe importance of this issue, this paper aims to analyze the contentpublished (news and comments) on the G1 News Portal with techniquesbased on data visualization and Natural Language Processing,such as sentiment analysis and topic modeling. The results showthat even with most of the comments being neutral or negative andclassified or not as hate speech, the majority of them were acceptedby the users.

scholarly journals SENTIMENT ANALYSIS FOR SARCASTIC MESSAGES IN SOCIAL MEDIA USING DEEP LEARNING TECHNIQUES - AN EMPIRICAL STUDY

2021 ◽  
Vol 9 (2) ◽  
pp. 1051-1052
Author(s):  
K. Kavitha, Et. al.
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Empirical Study ◽  
Sentiment Analysis ◽  
Learning Strategies ◽  
Language Processing ◽  
The People ◽  
Learning Techniques ◽  
Key Terms ◽  
Learning Frameworks

Sentiments is the term of opinion or views about any topic expressed by the people through a source of communication. Nowadays social media is an effective platform for people to communicate and it generates huge amount of unstructured details every day. It is essential for any business organization in the current era to process and analyse the sentiments by using machine learning and Natural Language Processing (NLP) strategies. Even though in recent times the deep learning strategies are becoming more familiar due to higher capabilities of performance. This paper represents an empirical study of an application of deep learning techniques in Sentiment Analysis (SA) for sarcastic messages and their increasing scope in real time. Taxonomy of the sentiment analysis in recent times and their key terms are also been highlighted in the manuscript. The survey concludes the recent datasets considered, their key contributions and the performance of deep learning model applied with its primary purpose like sarcasm detection in order to describe the efficiency of deep learning frameworks in the domain of sentimental analysis.

Discovery of Sustainable Transport Modes Underlying TripAdvisor Reviews With Sentiment Analysis

2021 ◽  
pp. 180-199
Author(s):  
Ainhoa Serna ◽  
Jon Kepa Gerrikagoitia
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Predictive Analytics ◽  
Data Gathering ◽  
Point Of View ◽  
Training Data ◽  
Complete Analysis ◽  
Sustainable Transport ◽  
Transport Modes

In recent years, digital technology and research methods have developed natural language processing for better understanding consumers and what they share in social media. There are hardly any studies in transportation analysis with TripAdvisor, and moreover, there is not a complete analysis from the point of view of sentiment analysis. The aim of study is to investigate and discover the presence of sustainable transport modes underlying in non-categorized TripAdvisor texts, such as walking mobility in order to impact positively in public services and businesses. The methodology follows a quantitative and qualitative approach based on knowledge discovery techniques. Thus, data gathering, normalization, classification, polarity analysis, and labelling tasks have been carried out to obtain sentiment labelled training data set in the transport domain as a valuable contribution for predictive analytics. This research has allowed the authors to discover sustainable transport modes underlying the texts, focused on walking mobility but extensible to other means of transport and social media sources.

Deep Learning Techniques for Polarity Classification in Multimodal Sentiment Analysis

2018 ◽  
Vol 17 (03) ◽  
pp. 883-910 ◽  
Author(s):  
P. D. Mahendhiran ◽  
S. Kannimuthu
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Relevant Information ◽  
Neural Net ◽  
Video Information ◽  
Automatic Feature Extraction ◽  
Multimodal Sentiment Analysis

Contemporary research in Multimodal Sentiment Analysis (MSA) using deep learning is becoming popular in Natural Language Processing. Enormous amount of data are obtainable from social media such as Facebook, WhatsApp, YouTube, Twitter and microblogs every day. In order to deal with these large multimodal data, it is difficult to identify the relevant information from social media websites. Hence, there is a need to improve an intellectual MSA. Here, Deep Learning is used to improve the understanding and performance of MSA better. Deep Learning delivers automatic feature extraction and supports to achieve the best performance to enhance the combined model that integrates Linguistic, Acoustic and Video information extraction method. This paper focuses on the various techniques used for classifying the given portion of natural language text, audio and video according to the thoughts, feelings or opinions expressed in it, i.e., whether the general attitude is Neutral, Positive or Negative. From the results, it is perceived that Deep Learning classification algorithm gives better results compared to other machine learning classifiers such as KNN, Naive Bayes, Random Forest, Random Tree and Neural Net model. The proposed MSA in deep learning is to identify sentiment in web videos which conduct the poof-of-concept experiments that proved, in preliminary experiments using the ICT-YouTube dataset, our proposed multimodal system achieves an accuracy of 96.07%.

Shifting sands

2013 ◽  
Vol 50 (3) ◽  
pp. 319-336 ◽  
Author(s):  
Stephen M Shellman ◽  
Brian P Levey ◽  
Joseph K Young
Keyword(s):  
Language Processing ◽  
The Philippines ◽  
Dynamic Interaction ◽  
Structural Factors ◽  
Dynamic Approach ◽  
Time Period ◽  
Mass Attitudes ◽  
The Masses ◽  
Processing Techniques ◽  
Available Information

Why does a dissident group go through phases of violence and nonviolence? Many studies of states and dissidents examine related issues by focusing on structural or rarely changing factors. In contrast, some more recent work focuses on dynamic interaction of participants. We suggest forecasting state–dissident interaction using insights from this dynamic approach while also incorporating structural factors. We explore this question by offering new data on the behavior of groups and governments collected using automated natural language processing techniques. These data provide information on who is doing what to whom at a directed-dyadic level. We also collected new data on the attitudes or sentiment of the masses using novel automated techniques. Since obtaining valid and reliable time-series public opinion data on mass attitudes towards a dissident group is extremely difficult, we have created automated sentiment data by scraping publicly available information written by members of the population and aggregating this information to create a pollof opinion at a discrete time period. We model the violence and nonviolence perpetrated by two groups: the Tamil Tigers in Sri Lanka and the Moro Islamic Liberation Front in the Philippines. We find encouraging results for predicting future phase shifts in violence when accounting for behaviors modeled with our data as opposed to models based solely on structural factors.

scholarly journals Sentiment Analysis using Optimized Feature Sets in Different Twitter Dataset Domains

2019 ◽  
Vol 8 (11) ◽  
pp. 3035-3039
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Support Vector ◽  
Text Analytics ◽  
K Nearest Neighbors ◽  
Verbal Language ◽  
Feature Sets ◽  
Goods And Services ◽  
Assessment Metrics

in the last years, the relevance of sentiment analysis is broad and dominant. The capability to take out insights from social data is a tradition that is being extensively accepted by all over globe. Sentiment Analysis has turn out to be a hot-trend issue of technical and marketplace research in the area of Natural Language Processing (NLP) and Machine Learning. Sentiment analysis is enormously useful in social media supervising as it permits us to expand an impression of the wider open estimation behind definite topics. Investigation of social media streams is typically limited to just essential sentiment analysis and count based metrics. This is of the same kind to just scratching the outside and missing out on those elevated value insight that is ahead of you to be discovered. There’s a lot of effort to be done, but perfections are being prepared every day. It is a way to appraise on paper or verbal language to settle on if the expression is favorable, unfavorable, or unbiased, and to what level. Today’s algorithm-based sentiment analysis tools can touch vast amount of client response constantly and precisely. Balancing with text analytics, sentiment analysis exposes the customer’s estimation concerning topics ranging from your goods and services to your position, your advertisements, or even your challengers. These efforts scrutinize the crisis of studying texts, like posts and reviews, uploaded by user on Twitter. The Support Vector Machine (SVM), k-nearest neighbors algorithm (KNN) and proposed optimized feature sets model is offered to progression the tweet features and to recognize the out of sight sentiments from these tweets. These essential concepts when used in combinations become a very significant tool for analyzing millions of variety conversations with human echelon accurateness. The projected optimized feature sets model Sentiment Analysis exercise the assessment metrics of Precision, Recall, F-score, and Accuracy. Also, average measures weighted F1-scores are constructive for categorization of Positive, Negative and Neutral multi-class problems. The running time of the technique is evaluates by accomplishing diverse methods in the same investigational setup consisting a cluster of 8 nodes. Planned optimized feature sets model Sentiment Analysis reachs 82 % accuracy as compare with SVM 78.6 % and KNN 75 %. Further, while analyzing sentiments of tweets we have measured only tweets in English acknowledged by Twitter streaming API.

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