Political Influence Analysis Social Media Text Mining for Public Opinion: Case Study Makassar City

Author(s):  
Jufri ◽  
Musdalifa Thamrin
2012 ◽  
Author(s):  
Douglas Yeung ◽  
Sara Beth Elson ◽  
Parisa Roshan ◽  
S. Bohandy ◽  
Alireza Nader
Keyword(s):  

Author(s):  
Y. D. Wang ◽  
T. Wang ◽  
X. Y. Ye ◽  
J. Q. Zhu ◽  
J. Lee

Social media have become a universal phenomenon in our society (Wang et al., 2012). As a new data source, social media have been widely used in knowledge discovery in fields related to health (Jackson et al., 2014), human behaviour (Lee, 2014), social influence (Hong, 2013), and market analysis (Hanna et al., 2011). <br><br> In this paper, we report a case study of the 2012 Beijing Rainstorm to investigate how emergency information was timely distributed using social media during emergency events. We present a classification and location model for social media text streams during emergency events. This model classifies social media text streams based on their topical contents. Integrated with a trend analysis, we show how Sina-Weibo fluctuated during emergency events. Using a spatial statistical analysis method, we found that the distribution patterns of Sina-Weibo were related to the emergency events but varied among different topics. This study helps us to better understand emergency events so that decision-makers can act on emergencies in a timely manner. In addition, this paper presents the tools, methods, and models developed in this study that can be used to work with text streams from social media in the context of disaster management.


2021 ◽  
Author(s):  
Fei Shen ◽  
Wenting Yu ◽  
Chen Min ◽  
Qianying Ye ◽  
Chuanli Xia ◽  
...  

Text mining has been a dominant approach to extracting useful information from massive unstructured data online. But existing tools for Chinese word segmentation are not ideal for processing social media text data in Cantonese. This project developed CyberCan (https://github.com/shenfei1010/CyberCan), a lexicon of contemporary Cantonese based on more than 100 million pieces of internet texts. We compared the performance of CyberCan with existing Mandarin and Cantonese lexicons in terms of their word segmentation performance. Findings suggest that CyberCan outperforms all existing lexicons by a considerable margin.


2020 ◽  
Vol 34 (5) ◽  
pp. 826-844 ◽  
Author(s):  
Louis Tay ◽  
Sang Eun Woo ◽  
Louis Hickman ◽  
Rachel M. Saef

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology


2021 ◽  
Author(s):  
Shriphani Palakodety ◽  
Ashiqur R. KhudaBukhsh ◽  
Guha Jayachandran

Sign in / Sign up

Export Citation Format

Share Document