Analysis of the Value Change of Virtual Influencers as Seen in the Press and Social Media Using Text Mining

The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.

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Confronting the digital mob: Press coverage of online justice seeking

European Journal of Communication ◽

10.1177/0267323120928234 ◽

2020 ◽

Vol 35 (6) ◽

pp. 597-612

Author(s):

Daniel Trottier

Keyword(s):

Social Media ◽

Press Coverage ◽

Social Actors ◽

The Public ◽

News Reports ◽

The Press ◽

Social Media Platforms ◽

The Uk

This article offers an exploratory account of press coverage of digitally mediated vigilantism. It considers how the UK press renders these events visible in a sustained and meaningful way. News reports and editorials add visibility to these events, and also make them more tangible when integrating content from social media platforms. In doing so, this coverage directs attention to a range of social actors, who may be perceived as responsible for these kinds of developments. In considering how other social actors are presented in relation to digital vigilantism, this study focusses on press accounts of those either initiating or being targeted by online denunciations, and also on a broader and often amorphous range of spectators to such events, often referred to as ‘internet mobs’. Relatedly, this article explores how specific practices related to digital vigilantism such as denunciation are expressed in press coverage, as well as coverage of motivations by the public to either participate or facilitate such practices. Reflecting on how the press represent mediated denunciation will illustrate not only how tabloids and broadsheets frame such practices, but also how they take advantage of connective and data-generating affordances associated with social platforms.

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Disaster Reporting and Alert System Using Tweets in a Social Media

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit195249 ◽

2019 ◽

pp. 176-181

Author(s):

P. Tamije Selvy ◽

V. Suriya Prakash ◽

S. Shriram ◽

N. Vimalesh

Keyword(s):

Social Media ◽

Text Mining ◽

Comparative Study ◽

Alert System ◽

Accuracy Rate ◽

The Social ◽

Short Period ◽

Disaster Reporting ◽

Centralized Database

The number of Social Media users have increased rapidly these days and a lot of valuable as well as non valuable information is shared in the social which is capable of reaching many people in a short period of time and hence the valuable information that are shared in the social media can be used for many types of analysis. In this paper the tweets that are shared in the name of a disaster is taken and then a alert system is build. This alert system gives alert to the users after checking the received data with the centralized database. This paper also gives a comparative study on the algorithm used in extracting the data from the social media which gives us the accuracy rate of different algorithm that can be used for text mining.

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Predicting real estate market trends and value using pre-processing and sentiment text mining analysis

Real Estate Economics Management ◽

10.22337/2073-8412-2021-1-35-43 ◽

2021 ◽

pp. 35-43

Author(s):

Nikolay Sinyak ◽

Singh Tajinder ◽

Jaglan Madhu Kumari ◽

Vitaliy Kozlovskiy

Keyword(s):

Social Media ◽

Text Mining ◽

Real Estate ◽

Real Estate Market ◽

Media Analysis ◽

Market Information ◽

Real Estate Markets ◽

The Real Estate ◽

Crowd Wisdom ◽

Market Trends

Ubiquitous growth in the text mining field is unprecedented, where social media mining is playing a significant role. Gigantic growth of text mining is becoming a potential source of crowd wisdom extraction and analysis especially in terms of text pre-processing and sentiment analysis. The analysis of a potential influence of sentiment on real estate markets controversially discussed by scholars of finance, valuation and market efficiency supporters. Therefore, it’s a significant task of current research purview which not only provide an appropriate platform for the contributors but also for active real estate market information seekers. Text mining has gained the widespread attention of real estate market information users which is almost on explosion level. Accessibility of data on such behemoth scale mandates regular and critical analysis of this information for various perspectives’ plausibility. Rich patterns of online social text can be exploited to extract the relevant real estate information effectively. As text mining plays a significant and crucial role in discovery of these insights therefore its challenges and contribution in social media analysis must be explored extensively. In this paper, we provide a brief about the current summary of the modern state of text mining in pre-processing and sentiment for the real estate market analysis. Empha-sis is placed on the resources and learning mechanism available to real estate researchers and practitioners, as well as the major text mining tasks of interest to the community. Thus, the main aim of this chapter is to expound and intellectualize the domains of social media which are accessible on an extraordinary range in the field of text mining real estate for predicting real estate market trends and value.

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PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING

Jurnal Gaussian ◽

10.14710/j.gauss.v10i4.30409 ◽

2022 ◽

Vol 10 (4) ◽

pp. 583-593

Author(s):

Syiva Multi Fani ◽

Rukun Santoso ◽

Suparti Suparti

Keyword(s):

Social Media ◽

Text Mining ◽

Virtual Networks ◽

Number Of Clusters ◽

Silhouette Coefficient ◽

Twitter Account ◽

Computer Based ◽

Twitter Users ◽

Clustering Data ◽

Coefficient Method

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.

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Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19

Generation Journal ◽

10.29407/gj.v5i2.16125 ◽

2021 ◽

Vol 5 (2) ◽

pp. 109-118

Author(s):

Euis Saraswati ◽

Yuyun Umaidah ◽

Apriade Voutama

Keyword(s):

Neural Network ◽

Social Media ◽

Artificial Neural Network ◽

Text Mining ◽

Ann Model ◽

Rapid Spread ◽

Backpropagation Method ◽

Artificial Neural ◽

The Government ◽

Artificial Neural Network Ann

Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.

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Web Text Mining

The Oxford Handbook of Computational Linguistics 2nd edition ◽

10.1093/oxfordhb/9780199573691.013.27 ◽

2016 ◽

Author(s):

Ricardo Baeza-Yates ◽

Roi Blanco ◽

Malú Castellanos

Keyword(s):

Social Media ◽

Text Mining ◽

Sentiment Analysis ◽

Web Search ◽

Internet Users ◽

Entity Retrieval ◽

Web Text Mining ◽

Text Content ◽

The Web

Web search has become a ubiquitous commodity for Internet users. This fact puts a large number of documents with plenty of text content at our fingertips. To make good use of this data, we need to mine web text. This triggers the two problems covered here: sentiment analysis and entity retrieval in the context of the Web. The first problem answers the question of what people think about a given product or a topic, in particular sentiment analysis in social media. The second problem addresses the issue of solving certain enquiries precisely by returning a particular object: for instance, where the next concert of my favourite band will be or who the best cooks are in a particular region. Where to find these objects and how to retrieve, rank, and display them are tasks related to the entity retrieval problem.

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