scholarly journals Business Intelligence from Social Media: A Study from the VAST Box Office Challenge

2014 ◽  
Vol 34 (5) ◽  
pp. 58-69 ◽  
Author(s):  
Yafeng Lu ◽  
Feng Wang ◽  
Ross Maciejewski
Keyword(s):  
Social Media ◽  
Business Intelligence ◽  
Box Office

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 analytics and business intelligence research: A systematic review

2020 ◽  
Vol 57 (6) ◽  
pp. 102279
Author(s):  
Jaewoong Choi ◽  
Janghyeok Yoon ◽  
Jaemin Chung ◽  
Byoung-Youl Coh ◽  
Jae-Min Lee
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Business Intelligence ◽  
Social Media Analytics

Electronic word-of-mouth, box office revenue and social media

2017 ◽  
Vol 22 ◽  
pp. 13-23 ◽  
Author(s):  
Hyunmi Baek ◽  
Sehwan Oh ◽  
Hee-Dong Yang ◽  
JoongHo Ahn
Keyword(s):  
Social Media ◽  
Word Of Mouth ◽  
Electronic Word Of Mouth ◽  
Box Office

scholarly journals A Novel Framework for Aspect Knowledgebase Generated Automatically from Social Media Using Pattern Rules

2021 ◽  
Vol 22 (4) ◽  
Author(s):  
Tuan Anh Tran ◽  
Jarunee Duangsuwan ◽  
Wiphada Wettayaprasit
Keyword(s):  
Social Media ◽  
Performance Evaluation ◽  
Semantic Similarity ◽  
Business Intelligence ◽  
High Performance ◽  
Part Of Speech ◽  
Benchmark Datasets ◽  
Second Procedure

One of the factors improving businesses in business intelligence is summarization systems which could generate summaries based on sentiment from social media. However, these systems could not produce automatically, they used annotated datasets. To automatically produce sentiment summaries without using the annotated datasets, we propose a novel framework using pattern rules. The framework has two procedures: 1) pre-processing and 2) aspect knowledgebase generation. The first procedure is to check and correct misspelt words (bigram and unigram) by a proposed method, and tag part-of-speech all words. The second procedure is to automatically generate aspect knowledgebase used to produce sentiment summaries by the sentiment summarization systems. Pattern rules and semantic similarity-based pruning are used to automatically generate aspect knowledgebase from social media. In the experiments, eight domains from benchmark datasets of reviews are used. The performance evaluation of our proposed approach shows the high performance when compared to other approaches.

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