scholarly journals Analisis Data Twitter: Ekstraksi dan Analisis Data G eospasial

Jurnal INKOM ◽  
2016 ◽  
Vol 10 (1) ◽  
pp. 27 ◽  
Author(s):  
Edi Surya Negara ◽  
Ria Andryani ◽  
Prihambodo Hendro Saksono
Keyword(s):  
Social Media ◽  
Data Analytics ◽  
Social Media Analytics ◽  
Twitter Data

Data geospasial pada media sosial Twitter dapat dimanfaatkan untuk mengetahui informasi spasial (lokasi) yang merupakan lokasi sumber munculnya persepsi publik terhadap sebuah isu di media sosial. Besarnya produksi data geospasial yang dihasilkan oleh Twitter memberikan peluang besar untuk dapat dimanfaatkan oleh berbagai pihak sehingga menghasilkan informasi yang lebih bernilai melalui proses Twitter Data Analytics. Proses pemanfaatan data geospasial Twitter dimulai dengan melakukan proses ekstraksi terhadap informasi spatial berupa titik koordinat pengguna Twitter. Titik koordinat pengguna Twitter didapatkan dari sharing location yang dilakukan oleh pengguna Twitter. Untuk mengekstrak dan menganalisis data geospasial pada Twitter dibutuhkan pengetahuan dan kerangka kerja tentang social media analytics (SMA). Pada penelitian ini dilakukan ekstraksi dan analisis data geospasial Twitter terhadap suatu isu publik yang sedang berkembang dan mengembangakan prototipe perangkat lunak yang digunakan untuk mendapatkan data geospasial yang ada pada Twitter. Proses ekstraksi dan analisis dilakukan melalui empat tahapan yaitu: proses penarikan data (crawling), penyimpanan (storing), analisis (analyzing), dan visualisasi (vizualizing). Penelitian ini bersifat exploratory yang terfokus pada pengembangan teknik ekstrasi dan analisis terhadap data geospasial twitter

scholarly journals Understanding an Urban Park through Big Data

2019 ◽  
Vol 16 (20) ◽  
pp. 3816 ◽  
Author(s):  
Jisoo Sim ◽  
Patrick Miller
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Planning Process ◽  
Big Data Analytics ◽  
Urban Park ◽  
Social Media Analytics ◽  
Forest Park ◽  
Planning And Design ◽  
Data Source

To meet the needs of park users, planners and designers must know what park users want to do and how they want the park to offer different activities. Big data may help planners and designers gain this knowledge. This study examines how big data collected in an urban park could be used to identify meaningful implications for planning and design. While big data have emerged as a new data source, big data have not become an accepted source of data due to a lack of understanding of big data analytics. By comparing a survey as a traditional data source with big data, this study identifies the strengths and weaknesses of using big data analytics in park planning and design. There are two research questions: (1) what activities do park users want; and (2) how satisfied are users with different activities. The Gyeongui Line Forest Park, which was built on an abandoned railway, was selected as the study site. A total of 177 responses were collected through the onsite survey, and 3703 tweets mentioning the park were collected from Twitter. Results from the survey show that ordinary activities such as walking and taking a rest in the park were the most common. These findings also support existing studies. The results from social media analytics found notable things such as positive tweets about how the railway was turned into a park, and negative tweets about diseases that may occur in the park. Therefore, a survey as traditional data and social media analytics as big data can be complementary methods for the design and planning process.

An Introduction to Data Analytics

2017 ◽  
pp. 1-14 ◽  
Author(s):  
A. Sheik Abdullah ◽  
S. Selvakumar ◽  
A. M. Abirami
Keyword(s):  
Social Media ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Analytical Techniques ◽  
Educational Institutions ◽  
Social Media Analytics ◽  
Text Analytics ◽  
Raw Data ◽  
Industrial Organizations ◽  
Prescriptive Analytics

Data analytics mainly deals with the science of examining and investigating raw data to derive useful patterns and inference. Data analytics has been deployed in many of the industries to make decisions at proper levels. It focuses upon the assumption and evaluation of the method with the intention of deriving a conclusion at various levels. Various types of data analytical techniques such as predictive analytics, prescriptive analytics, descriptive analytics, text analytics, and social media analytics are used by industrial organizations, educational institutions and by government associations. This context mainly focuses towards the illustration of contextual examples for various types of analytical techniques and its applications.

scholarly journals Optimizing Search and Data Analytics of Twitter Data using Elastic Search Algorithms

2020 ◽  
Vol 9 (6) ◽  
pp. 427-433
Keyword(s):  
Social Media ◽  
Business Intelligence ◽  
Data Analytics ◽  
Distributed Search ◽  
Time Data ◽  
Social Media Data ◽  
Twitter Data ◽  
Crucial Part ◽  
Media Data ◽  
Better Than

Quick data acquisition and analysis became an important tool in the contemporary era. Real time data is made available in World Wide Web (WWW) and social media. Especially social media data is rich in opinions of people of all walks of life. Searching and analysing such data provides required business intelligence (BI) for applications of various domains in the real world. The application may be in the area of politics or banking or insurance or healthcare industry. With the emergence of cloud computing, volumes of data are added to cloud storage infrastructure and it is growing exponentially. In this context, Elasticsearch is the distributed search and analytics engine that is very crucial part of Elastic Stack. For data collection, aggregation and enriching it Beats and Logstash are used and such data is stored in Elasticsearch. For interactive exploration and visualization Kibana is used. Elasticsearch helps in indexing of data, searching efficiently and performing data analytics. In this paper, the utility of Elasticsearch is evaluated for optimising search and data analytics of Twitter data. Empirical study is made with the Elasticsearch tool configured for Windows and also using Amazon Elasticsearch and the results are compared with state of art. The experimental results revealed that the Elasticsearch performs better than the existing ones.

Redis-Based Messaging Queue and Cache-Enabled Parallel Processing Social Media Analytics Framework

2020 ◽  
Author(s):  
Ravindra Kumar Singh ◽  
Harsh Kumar Verma
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Real Time ◽  
Data Analytics ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Social Media Analytics ◽  
Data Engineering ◽  
Extreme Gradient Boosting

Abstract The extensive usage of social media polarity analysis claims the need for real-time analytics and runtime outcomes on dashboards. In data analytics, only 30% of the time is consumed in modeling and evaluation stages and 70% is consumed in data engineering tasks. There are lots of machine learning algorithms to achieve a desirable outcome in prediction points of view, but they lack in handling data and their transformation so-called data engineering tasks, and reducing its time remained still challenging. The contribution of this research paper is to encounter the mentioned challenges by presenting a parallelly, scalable, effective, responsive and fault-tolerant framework to perform end-to-end data analytics tasks in real-time and batch-processing manner. An experimental analysis on Twitter posts supported the claims and signifies the benefits of parallelism of data processing units. This research has highlighted the importance of processing mentioned URLs and embedded images along with post content to boost the prediction efficiency. Furthermore, this research additionally provided a comparison of naive Bayes, support vector machines, extreme gradient boosting and long short-term memory (LSTM) machine learning techniques for sentiment analysis on Twitter posts and concluded LSTM as the most effective technique in this regard.

scholarly journals Lupus patients on Twitter: Expressed symptoms and attitudes toward using the platform for healthcare engagement (Preprint)

2019 ◽  
Author(s):  
Alden Bunyan ◽  
Swamy Venuturupalli ◽  
Katja Reuter
Keyword(s):  
Social Media ◽  
Data Extraction ◽  
Analysis Data ◽  
Self Report ◽  
Social Media Analytics ◽  
Translational Science ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Twitter Data ◽  
The U.S

BACKGROUND Lupus is a complex autoimmune disease that is difficult to diagnose and treat. It is estimated that at least 5 million Americans have lupus, with more than 16,000 new cases of lupus being reported annually in the U.S. Social media provides a platform for patients to find rheumatologists, peers, and build awareness of the condition. Researchers suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues. However, there is a lack of research about the characteristics of lupus patients on Twitter and their attitudes toward using Twitter for engaging them with their healthcare. OBJECTIVE This study has two objectives: (1) to conduct a content analysis of Twitter data published by users (in English) in the U.S. between 9/1/2017 and 10/31/2018 to identify patients who publicly discuss their lupus condition and to assess their expressed health themes, and (2) to conduct a cross-sectional survey among these lupus patients on Twitter to study their attitudes toward using Twitter for engaging them with their healthcare. METHODS This is a mixed-methods study that analyzes retrospective Twitter data and conducts a cross-sectional survey among lupus patients on Twitter. We will use Symplur Signals, a healthcare social media analytics platform, to access the Twitter data and analyze user-generated posts that include keywords related to lupus. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among lupus patients. We will further conduct self-report surveys via Twitter by inviting all identified lupus patients who discuss their lupus condition on Twitter. The goal of the survey is to collect data about the characteristics of lupus patients (e.g., gender, race/ethnicity, educational level) and their attitudes toward using Twitter for engaging them with their healthcare. RESULTS This study has been funded by the National Center for Advancing Translational Science (NCATS) through a Clinical and Translational Science Award (CTSA) award. The Institutional Review Board at the University of Southern California (HS-19-00048) approved the study. Data extraction and cleaning are complete. We obtained 47,715 Twitter posts containing terms related to “lupus” from users in the U.S. published in English between 9/1/2017 and 10/31/2018. We will include 40,885 posts in the analysis. Data analysis will be completed by the end of 2019. CONCLUSIONS The data obtained in this pilot study will shed light on whether Twitter provides a promising data source for garnering health-related attitudes among lupus patients. The data will also help to determine whether Twitter might serve as a potential outreach platform for raising awareness of lupus among patients and healthcare providers and implementing related health education interventions. CLINICALTRIAL N/A

Applying Data Analytics to Social Media Advertising: A Twitter Advertising Campaign Case Study

2017 ◽  
Vol 21 (1) ◽  
pp. 26-32 ◽  
Author(s):  
Shanshan Lou
Keyword(s):  
Social Media ◽  
Data Analytics ◽  
Secondary Data ◽  
Research Direction ◽  
Future Research ◽  
Social Media Analytics ◽  
Advertising Campaign ◽  
Social Media Advertising

This paper presents a class case study of an assignment that asked students to use a Twitter follower report to design a Twitter advertising campaign. The purpose of this case study is to immerse students in a real social media environment and help them become familiar with analyzing social media data to develop advertising campaigns. Students' interview responses suggest that incorporating a project that requires social media analytics techniques in an advertising class can help them better understand the role of secondary research and database analysis in developing consumer profiles and making campaign decisions. The findings also suggest that students have a strong desire to work with secondary data in designing social media advertising campaigns. The advantages of data analytics should be further explored in advertising campaign classes to help students become successful campaign designers. Limitations and future research direction are also discussed.

Data Analytics for Better Branding of E-Governance and E-Business Systems

2020 ◽  
pp. 51-78
Author(s):  
Rajan Gupta ◽  
Saibal Kumar Pal ◽  
Sunil Kumar Muttoo
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Data Analysis ◽  
Data Analytics ◽  
Descriptive Statistics ◽  
Social Media Analytics ◽  
Business Systems ◽  
Government Programs ◽  
The Public ◽  
Ranking Analysis

Efficient e-governance leads to stronger democracy which can be achieved through higher trust, visibility, and transparency in the system, which can be acquired through effective branding. Various techniques of data analytics can help in achieving trust and transparency in the system. The objective of the study is to resolve various issues in the public sector through analytics-based improvement of different parameters of branding, namely, communication, consistency, clarity, and competition. The research design of the study is a combination of both qualitative and quantitative techniques like descriptive statistics. The main techniques emerged for data analysis includes rating and ranking analysis of government apps, social media analytics, text and speech analytics, media analytics, statistical analytics and data mining, telecom analytics, and people demographics for government programs. It was found that the “Digital India” campaign under e-governance initiative was highly successful based on different kinds of analytical methods found in the study.

scholarly journals A Review on Big Data Analytics Via Social Media

2019 ◽  
pp. 299-302
Keyword(s):  
Social Media ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Social Media Analytics ◽  
Data Mapping ◽  
Social Data ◽  
Advantages And Disadvantages ◽  
Business Change ◽  
Social Media Platforms ◽  
The Right

Analytics is very important in all fields in order to make decisions over certain facts. Social media analytics is the process of collecting information from various social media platforms, websites and blogs. These analytics is done to make effective business conclusions. The usage of social media has become the latest trend in today’s world. Social data analytics is not about just collecting likes and comments shared by individuals but it has become the platform for many trademarks to bring out promotion. Applications such as marketing, elections widely used social data to make predictive decisions. Some of the approaches followed are forming hypothesis, getting deep into the data, mapping events etc. These analytics can also be done in applications such as business, Change in amendments, Education, Demonetization etc. The challenges faced are metrics formed by social media should reach the right people, unstructured data being difficult to priestship paper discusses about the model, theme, performance evaluation, advantages and disadvantages under literature survey.

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