Exploring Cryptocurrency Sentiments With Clustering Text Mining on Social Media

2021 ◽  
pp. 157-171
Author(s):  
Jiwen Fang ◽  
Dickson K. W. Chiu ◽  
Kevin K. W. Ho
Keyword(s):  
Social Media ◽  
Text Mining ◽  
User Satisfaction ◽  
Communication Platform ◽  
Word Cloud ◽  
General User ◽  
Mass Information

Social media has become a popular communication platform and aggregated mass information for sentimental analysis. As cryptocurrency has become a hot topic worldwide in recent years, this chapter explores individuals' behavior in sharing Bitcoin information. First, Python was used for extracting around one month's set of Tweet data to obtain a dataset of 11,674 comments during a month of a substantial increase in Bitcoin price. The dataset was cleansed and analyzed by the process documents operator of RapidMiner. A word-cloud visualization for the Tweet dataset was generated. Next, the clustering operator of RapidMiner was used to analyze the similarity of words and the underlying meaning of the comments in different clusters. The clustering results show 85% positive comments on investment and 15% negative ones to Bitcoin-related tweets concerning security. The results represent the generally bullish environment of the cryptocurrency market and general user satisfaction during the period concerned.

COMMUNICATING FIVE-YEAR BUDGETS FOR THE INDIAN ECONOMY: COMPARATIVE TEXT AND SENTIMENT ANALYSIS

2021 ◽  
Vol 14 (8) ◽  
pp. 133-144
Author(s):  
Neelam Kaushal ◽  
Suman Ghalawat ◽  
Apul Saroha
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Social Issues ◽  
R Software ◽  
Text Data ◽  
Indian Economy ◽  
Word Cloud ◽  
Annual Budget ◽  
Sentiment Score

The content on social media is full of useful information that helps in communicating people’s preferences and opinions. The various examples in this context are that people frequently express their opinions about films and other social issues using Twitter, Facebook, etc. In this work, Sentiment Analysis of the Annual Budget for five financial years, namely, 2017–2018, 2018–2019, 2019–2020, 2020–2021, and 2021–2022 was initiated with the help of Twitter. Firstly, the researcher applied Text Mining to extract the budget's text data documents and computed correlation to know the association of influential words. Then, in analysis section plotted the occurrence of the words and the accompanying word cloud. The analysis was performed employing R software. Finally, the sentiment score for each item was calculated and assessed. This research is crucial because conducting a comparative text and Sentiment Analysis of five-year budgets for the Indian economy would communicate the previously prevailing positive and negative forecasts and thinking, which will aid future policymakers in planning future budgets.

Success Factors of Social Media Applications (SMA): Case Study among UUM Students

2021 ◽  
Vol 12 (3) ◽  
pp. 2733-2745
Author(s):  
Mazlan Mohd Sappri Et.al
Keyword(s):  
Social Media ◽  
Service Quality ◽  
User Satisfaction ◽  
Mobile Application ◽  
Information Quality ◽  
Success Factors ◽  
Reference Model ◽  
Mobile App ◽  
Media Applications

Social media application (SMA) shows several important functions that causing theincrement of usage among mobile application or mobile app users, especially among18 to 28 years-old users. This causing several developers to create their own SMA thathave been targeted to mobile app users. However, only several SMA managed tobecome popular and successful in term of usage, leaving other unpopular SMA in thelower rank of the Google PlayStore. SMA created by developer in Malaysia face thesame situation as mentioned before where those SMA were supposed to attractMalaysian mobile users more. To assess this situation, this study aims to identify thesuccess factors of SMA usage and develop a set of metric based on the success factorsusing research model that have been developed in the past. Information SystemSuccess Model (ISSM) were studied and chosen as the reference model for this studybecause the model is suitable and have been used by other researchers in studiesregarding social media and SMA. ISSM contains several success factors like systemquality, service quality and information quality that affect the user satisfaction and useof a system, where this model were modified in this study with the addition ofnetworking quality and perceive privacy factors. This study were conducted on 380Universiti Utara Malaysia (UUM) students and after analysing the data collected, allproposed success factors except of service quality were found to have a positive impacttowards user satisfaction and usage. The success factors were included in the metricdesign and the metric were presented in an evaluation form for SMA developer inMalaysia to evaluate and applied the metric in their SMA.

Subjective Text Mining for Arabic Social Media

2017 ◽  
Vol 13 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Nourah F. Bin Hathlian ◽  
Alaaeldin M. Hafez
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Research Area ◽  
Arabic Text ◽  
Classification Methods ◽  
Learning Mechanisms ◽  
Subjective Analysis ◽  
Related Information ◽  
Data Source ◽  
Rich Content

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.

scholarly journals Disaster Reporting and Alert System Using Tweets in a Social Media

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.

scholarly journals Hubungan Motif dan Kepuasan Penggunaan Media Sosial Instagram di Mahasiswa Komunikasi Penyiaran Islam Angkatan 2014-2017

2019 ◽  
Vol 2 (2) ◽  
pp. 74-78
Author(s):  
Ngudi Ambar Sari ◽  
Bukhari Bukhari ◽  
Usman Usman ◽  
Prima Kurinati Hamzah
Keyword(s):  
Social Media ◽  
User Satisfaction ◽  
Quantitative Research ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Quantitative Research Methods ◽  
The Public ◽  
Media Usage ◽  
Active User ◽  
The Relationship

Instagram is one of the most popular social media for the public. One difference between Instagram and other social media, is that instamam is more likely to be used to find information and share information with users than to interact directly with fellow users. The purpose of this study is to find out and explain the motives and active user satisfaction in using Instagram social media and find out the relationship between the motives and satisfaction of Instagram social media usage. This study uses use and gratification theory which assumes that individuals have certain goals in using media. The method used in this research is quantitative research methods. The data collection tool is the questionnaire has been validated. The research sample was 70 people. The sampling technique is simple random sampling. The statistic test that the researchers used was the Partial Correlation Test (Pearson Product Moment). Data is processed using SPSS version 20. The results of this study indicate users want to get information and knowledge that is happening at the present time. Information satisfaction becomes the most obtained by Instagram social media users. Overall Instagram social media has given satisfaction to users and there is a significant relationship between motives and satisfaction.

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

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.

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

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.

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

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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