47,784 research outputs found
An Investigation of Predictors of Information Diffusion in Social Media: Evidence from Sentiment Mining of Twitter Messages
Social media have facilitated information sharing in social networks. Previous research shows that sentiment of text influences its diffusion in social media. Each emotion can be located on a three-dimensional space formed by dimensions of valence (positive–negative), arousal (passive / calm–active / excited), and tension (tense–relaxed). While previous research has investigated the effect of emotional valence on information diffusion in social media, the effect of emotional arousal remains unexplored. This study examines how emotional arousal influences information diffusion in social media using a sentiment mining approach. We propose a research model and test it using data collected from Twitter
5G Network in Content Based Emotion Detection by Sentimental Analysis Integrated with Opinion Mining and Deep Learning Architectures
The rapid growth of social networking sites in the Internet era has made them a necessary tool for sharing emotions with the entire world. To extract emotions from text, a variety of tools and approaches are available in fields of opinion mining as well as sentiment analysis. These researches propose novel technique opinion mining based emotion detection from the input social content using deep learning architectures. Here the input has been obtained as social media content based on opinion miningby 5G networks. The input has been processed for noise removal, smoothening and normalization. This processed input has been segmented using Markov model based convolutional neural networks (MMCNN). The segmented data has been classified using Canonical Correlation AnalysisBayesian neural network.An opinion mining method that analyzes statements regarding computer programming and predicts or recognizes their polarity was implemented, along with an earlier module that was integrated into an intelligent learning environment. These three steps made up the creation of the module. We assessed the corpus, text polarity precision, and emotion recognition. Experimental analysis has been carried out for various social media content collected by opinion mining in terms of accuracy, precision, recall, F-1 score, AUC.Proposed technique attained accuracy of 99%, precision of 96%, recall of 96%, F-1 score of 95%, AUC of 89%
Data extraction and preparation to perform a sentiment analysis using open source tools: the example of a Facebook fashion brand page
Social Media is a subject that is being very discussed in the present. The increasing availability of internet and the growth of Social Media platforms drove organizations attention to Opinion Mining and Sentiment Analysis. One of the most popular Social Media Platform is Facebook. In this Social Media Platform users/consumers can express their feelings with comments or with emotion buttons. With the functionalities of Facebook, they can criticize, praise, suggest or expect. The user's interactions with a brand page as posts, likes, shares or comments are getting more relevance in present days. The analysed data can give to decision-makers new approaches to run their business, understand their brand value against the competitors or even understand better what are the evaluations of customers or potential customers about the brand products or services. The purpose of this paper is to explain how to extract and prepare data collected in Facebook using open-source tools to perform a Sentiment Analysis.info:eu-repo/semantics/acceptedVersio
Multidimensional opinion mining from social data
Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This thesis focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm, and irony, from user-generated content represented across multiple social media platforms and in various media formats, like textual, visual, and audio. Mining people’s social opinions from social sources, such as social media platforms and newswires commenting
sections, is a valuable business asset that can be utilised in many ways and in multiple domains, such as Politics, Finance, and Government. The main objective of this research is to investigate how a multidimensional approach to Social Opinion Mining affects fine-grained opinion search and summarisation at an aspect-based level and whether such a multidimensional approach outperforms single dimension approaches in the context of an extrinsic human evaluation conducted in a real-world context: the Malta Government Budget, where five social opinion dimensions are taken into consideration, namely subjectivity, sentiment polarity, emotion, irony, and sarcasm. This human evaluation determines whether the multidimensional opinion summarisation results provide added-value to potential end-users, such as policy-makers and decision-takers, thereby providing a nuanced voice to the general public on their social opinions on topics of a national importance. Results obtained indicate that a more fine-grained aspect-based opinion summary based on the combined dimensions of subjectivity, sentiment polarity, emotion, and sarcasm or
irony is more informative and more useful than one based on sentiment polarity only. This research contributes towards the advancement of intelligent search and information retrieval from social data and impacts entities utilising Social Opinion Mining results towards effective policy formulation, policy-making, decision-making, and decision-taking at
a strategic level
Classification of Sentiment Analysis on Tweets using Machine Learning Techniques
Growth in social media has huge of amount of data which includes reviews about products ,blogs which discuss on the peoples opinion .We can learn sentiment analysis in web mining, data mining ,it is an application of Natural Language Processing. Due to growth in social media all the fortune companies are working on Opinion mining. The basic goal of Sentiment analysis is to ensure the sentence either as positive emotion or negative emotion. Sentiment analysis extracts the sentiments in the form various discussions, forums, blogs. Importance of social media leads in growth of sentiment analysis. For an organization, it wants to know about the people’s opinions on products which it had been released and it conducts surveys of products and opinion polls. Consumers also used to make research on products and price of product by using sentiment analysis. Marketers used to make research about company and products by effective utilization of sentiment analysis. This thesis contributes to classification of tweets in to either positive or negative using Machine learning techniques such as Nave Bayes classifier, Multinomial Nave Bayes algorithm, SVM Classifier, and Decision Tree. Comparative tabulation of performance of above mentioned classifiers is created to critically analyze the sentiment of tweets
Tourist Sentiment Mining Based on Deep Learning
Mining the sentiment of the user on the internet via the context plays a significant role in uncovering the human emotion and in determining the exactness of the underlying emotion in the context. An increasingly enormous number of user-generated content (UGC) in social media and online travel platforms lead to development of data-driven sentiment analysis (SA), and most extant SA in the domain of tourism is conducted using document-based SA (DBSA). However, DBSA cannot be used to examine what specific aspects need to be improved or disclose the unknown dimensions that affect the overall sentiment like aspect-based SA (ABSA). ABSA requires accurate identification of the aspects and sentiment orientation in the UGC. In this book chapter, we illustrate the contribution of data mining based on deep learning in sentiment and emotion detection
Emotion Dynamics of Public Opinions on Twitter
[EN] Recently, social media has been considered the fastest medium for information broadcasting and sharing. Considering the wide range of applications such as viral marketing, political campaigns, social advertisement, and so on, influencing characteristics of users or tweets have attracted several researchers. It is observed from various studies that influential messages or users create a high impact on a social ecosystem. In this study, we assume that public opinion on a social issue on Twitter carries a certain degree of emotion, and there is an emotion flow underneath the Twitter network. In this article, we investigate social dynamics of emotion present in users' opinions and attempt to understand (i) changing characteristics of users' emotions toward a social issue over time, (ii) influence of public emotions on individuals' emotions, (iii) cause of changing opinion by social factors, and so on. We study users' emotion dynamics over a collection of 17.65M tweets with 69.36K users and observe 63% of the users are likely to change their emotional state against the topic into their subsequent tweets. Tweets were coming from the member community shows higher influencing capability than the other community sources. It is also observed that retweets influence users more than hashtags, mentions, and replies.The work described in this article was carried out in the OSiNT Lab (https://www.iitg.ac.in/cseweb/osint/), Indian Institute of Technology Guwahati, India. The creation of the dataset used in this study was partly supported by the Ministry of Information and Electronic Technology, Government of India.Naskar, D.; Singh, SR.; Kumar, D.; Nandi, S.; Onaindia De La Rivaherrera, E. (2020). Emotion Dynamics of Public Opinions on Twitter. ACM Transactions on Information Systems. 38(2):1-24. https://doi.org/10.1145/3379340124382Ahmed, S., Jaidka, K., & Cho, J. (2016). 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Privacy and Security Concerns Associated with mHealth Technologies: A Computational Social Science Approach
mHealth technologies seek to improve personal wellness; however, there are still significant privacy and security challenges. The purpose of this study is to analyze tweets through social media mining to understand user-reported concerns associated with mHealth devices. Triangulation was conducted on a representative sample to confirm the results of the topic modeling using manual coding. The results of the emotion analysis showed 67% of the posts were largely associated with anger and fear, while 71% revealed an overall negative sentiment. The findings demonstrate the viability of leveraging computational techniques to understand the social phenomenon in question and confirm concerns such as accessibility of data, lack of data protection, surveillance, misuse of data, and unclear policies. Further, the results extend existing findings by highlighting critical concerns such as users’ distrust of these mHealth hosting companies and the inherent lack of data control
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