scholarly journals Business intelligence for social media interaction in the travel industry in Indonesia

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Michael Yulianto ◽  
Abba Suganda Girsang ◽  
Reinert Yosua Rumagit
Keyword(s):  
Social Media ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Marketing Activity ◽  
Social Media Data ◽  
Current Technology ◽  
Media Interaction ◽  
Travel Industry ◽  
Media Data ◽  
Analyze Data

Electronic ticket (eticket) provider services are growing fast in Indonesia, makingthe competition between companies increasingly intense. Moreover, most of them have the sameservice or feature for serving their customers. To get back the feedback of their customers, manycompanies use social media (Facebook and Twitter) for marketing activity or communicatingdirectly with their customers. The development of current technology allows the company totake data from social media. Thus, many companies take social media data for analyses. Thisstudy proposed developing a data warehouse to analyze data in social media such as likes,comments, and sentiment. Since the sentiment is not provided directly from social media data,this study uses lexicon based classification to categorize the sentiment of users’ comments. Thisdata warehouse provides business intelligence to see the performance of the company based ontheir social media data. The data warehouse is built using three travel companies in Indonesia.As a result, this data warehouse provides the comparison of the performance based on the socialmedia data.

Social Media and Corporate Data Warehouse Environments

2012 ◽  
Vol 3 (2) ◽  
pp. 1-12 ◽  
Author(s):  
Debora S. Bartoo
Keyword(s):  
Social Media ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Data Warehouses ◽  
Social Media Data ◽  
Hands On ◽  
Media Data

This paper argues that organizations need to prepare for the integration of social media data into their data warehouses in order to fully understand their customers. Social media has quickly gained acceptance in its adoption and use and firms are eager to get their hands on it to better understand customer sentiment. However, social media data is different and more complex than traditional data and most data warehouses are not structured in a way for BI applications to easily make sense it. As a result, it is becoming critical for business intelligence teams to begin to understand the challenges this data presents and to better plan for the integration of this information into corporate data warehouses.

scholarly journals Large Scale System for Social Media Data Warehousing

2022 ◽  
Vol 18 (1) ◽  
pp. 0-0
Keyword(s):  
Social Media ◽  
Information Extraction ◽  
Data Warehouse ◽  
Large Scale ◽  
Data Warehousing ◽  
Very High Frequency ◽  
Social Media Data ◽  
Large Scale System ◽  
Linguistic Rules ◽  
Media Data

Social media data become an integral part in the business data and should be integrated into the decisional process for better decision making based on information which reflects better the true situation of business in any field. However, social media data are unstructured and generated in very high frequency which exceeds the capacity of the data warehouse. In this work, we propose to extend the data warehousing process with a staging area which heart is a large scale system implementing an information extraction process using Storm and Hadoop frameworks to better manage their volume and frequency. Concerning structured information extraction, mainly events, we combine a set of techniques from NLP, linguistic rules and machine learning to succeed the task. Finally, we propose the adequate data warehouse conceptual model for events modeling and integration with enterprise data warehouse using an intermediate table called Bridge table. For application and experiments, we focus on drug abuse events extraction from Twitter data and their modeling into the Event Data Warehouse.

scholarly journals REALISASI TINDAK KESANTUNAN POSITIF DALAM WACANA AKADEMIK DI MEDIA SOSIAL BERPERSPEKTIF HUMANIS/POSITIVE POLITENESS OF ACADEMIC DISCOURSE TAKING PLACE IN SOCIAL MEDIA INTERACTION: A HUMANITY PERSPECTIVE

Aksara ◽  
2021 ◽  
Vol 32 (2) ◽  
pp. 323-338
Author(s):  
Hari Kusmanto ◽  
Nadia Puji Ayu ◽  
Harun Joko Prayitno ◽  
Laili Etika Rahmawati ◽  
Dini Restiyanti Pratiwi ◽  
...  
Keyword(s):  
Social Media ◽  
Academic Discourse ◽  
Human Values ◽  
Social Media Data ◽  
Positive Communication ◽  
Media Interaction ◽  
Data Source ◽  
High Attitude ◽  
Equivalent Method ◽  
Media Data

Abstrak Studi ini bertujuan mendeskripsikan wujud kesantunan berkomunikasi dalam media sosial WhatsApp antara mahasiswa dan dosen. Studi ini adalah kualitatif. Data dalam studi ini adalah kalimat-kalimat santun dalam wacana akademik di media sosial. Sumber data dalam studi ini adalah tuturan wacana akademik di media sosial. Pengumpulan data dalam studi ini menggunakan metode dokumentasi, simak, dan dilanjutkan dengan teknik catat. Analisis data dalam studi ini dilakukan dengan metode padan intralingual; padan pragmatis dan diperkuat dengan teknik analisis kesantunan Brown dan Levinson berperspektif humanis. Hasil studi ini menunjukkan tindak kesantunan positif meliputi: (1) mengucapkan terima kasih sebagai penghormatan kepada mitra tutur, 48%; (2) memberikan pertanyaan sebagai wujud perhatian kepada mitra tutur, 8%; (3) memberikan informasi kepada mitra tutur sebagai wujud kepedulian, 18%; (4) menunjukkan keoptimisan kepada mitra tutur supaya termotivasi, 4%; (5) memberikan hadiah kepada mitra tutur dengan memberikan dukungan, 4%; (6) mengucapkan salam kepada mitra tutur sebagai upaya mendoakan kebaikan kepada mitra tutur, 8%; dan (7) menggunakan penanda identitas sebagai wujud menjalin solidaritas antara penutur dan mitra tutur, 10%. Hal ini menunjukkan mahasiswa memiliki sikap penghormatan yang tinggi kepada dosen dengan menunjukkan komunikasi bernada positif. Tindak kesantunan mengucapkan terima kasih, memberikan informasi yang dibutuhkan mitra tutur, menunjukkan sikap percaya diri, mengucapkan salam merupakan wujud komunikasi yang berperspektif humanis, yakni menjunjung nilai-nilai kemanusian. Penelitian ini bermanfaat dalam membangun komunikasi pembelajaran yang berorientasi pada kesantunan berbahasa yang memartabatkan nilai-nilai humanitas dalam pembelajaran. Kata kunci: kesantunan positif, akademik, media sosial, humanis Abstract This study aims to describe the form of politeness in communicating on WhatsApp social media between students and lecturers. This study is qualitative. The data in this study are polite sentences in academic discourse on social media. The data source in this study is the speech of academic discourse on social media. Data collection in this study uses the documentation method, refer to it, and proceed with note taking technique. Data analysis in this study was carried out using the intralingual equivalent method; pragmatic equivalent and strengthened by Brown and Levinson’s politeness analysis techniques with a sweet perspective. The results of this study show positive politeness actions include: (1) Thank you for the speech partner observer 48%; (2) giving questions as a form of attention to the speech partners 8%; (3) providing information to the speech partners as a form of concern 18%; (4) showing optimism for the speech partners to be motivated 4%; (5) giving gifts to speech partners by giving support 4%; (6) greeting the speech partners in an effort to pray for the kindness of the speech partners 8%; and (7) using identity markers as a form of establishing solidarity between the speaker and the speech partner 10%.. ISSN 0854-3283 (Print), ISSN 2580-0353 (Online) , Vol. 32, No. 2, Desember 2020 323 Realisasi Tindak Kesantunan Positif dalam Wacana Akademik di Media Sosial Berperspektif Humanitas Halaman 323 — 338 (Hari Kusmanto, Nadia P. Ayu, Harun J. Prayitno, Laili E. Rahmawati, Dini R. Pratiwi, dan Tri Santoso) This shows students have a high attitude of respect for lecturers by showing positive communication. Actions of thanksgiving, giving information needed by the speech partner, showing self-con dence, greeting is a form of communication with a humanist perspective, namely upholding human values. This research is useful in building learning communication that is oriented towards language politeness that digni es human values in learning. Keywords: positive politeness, academic, social media, humanity 

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.

Managing extracted knowledge from big social media data for business decision making

2017 ◽  
Vol 21 (2) ◽  
pp. 275-294 ◽  
Author(s):  
Wu He ◽  
Feng-Kwei Wang ◽  
Vasudeva Akula
Keyword(s):  
Social Media ◽  
Big Data ◽  
Business Intelligence ◽  
Business Decisions ◽  
Business Decision ◽  
Social Media Data ◽  
Content Type ◽  
Media Data ◽  
Big Data Technology

Purpose This paper aims to propose a knowledge management (KM) framework for leveraging big social media data to help interested organizations integrate Big Data technology, social media and KM systems to store, share and leverage their social media data. Specifically, this research focuses on extracting valuable knowledge on social media by contextually comparing social media knowledge among competitors. Design/methodology/approach A case study was conducted to analyze nearly one million Twitter messages associated with five large companies in the retail industry (Costco, Walmart, Kmart, Kohl’s and The Home Depot) to extract and generate new knowledge and to derive business decisions from big social media data. Findings This case study confirms that this proposed framework is sensible and useful in terms of integrating Big Data technology, social media and KM in a cohesive way to design a KM system and its process. Extracted knowledge is presented visually in a variety of ways to discover business intelligence. Originality/value Practical guidance for integrating Big Data, social media and KM is scarce. This proposed framework is a pioneering effort in using Big Data technologies to extract valuable knowledge on social media and discover business intelligence by contextually comparing social media knowledge among competitors.

scholarly journals Forensic Analysis of Social Media Data : Research Challenges and Directions

2020 ◽  
Author(s):  
Muhammad Firdaus
Keyword(s):  
Social Media ◽  
Law Enforcement ◽  
Forensic Analysis ◽  
Digital Information ◽  
Privacy Rights ◽  
Law Enforcement Agencies ◽  
Social Media Data ◽  
Valid Evidence ◽  
Media Data ◽  
Analyze Data

The challenge to analyze data from social media sources not only for businesses and organizations but also for Law Enforcement Agencies. Social media offers various avenues for the collection and use of its data as evidence within a digital forensics investigation. The trail of digital information on social media, if explored correctly, can offer remarkable support in the criminal investigation. Social media evidence must be collected using careful, correct procedures and in a manner that ensures its integrity. Hence, social media evidence must be collected by a legally and scientifically appropriate forensic process and also coincide with the privacy rights of individuals. Following the legal process is a challenging task for legal practitioners and investigators to be able to carry out effective investigations and gather valid evidence efficiently. This paper explains the existing conditions of evidence acquisition, admissibility, and jurisdiction mechanisms in forensic social media. It also illustrates the direct challenges in gathering, analyzing, presenting, and validating evidence from social media data in the law enforcement process.

Sign in / Sign up

Export Citation Format

Share Document