TY - JOUR
T1 - Analyzing public sentiments online
T2 - combining human- and computer-based content analysis
AU - Su, Leona Yi Fan
AU - Cacciatore, Michael A.
AU - Liang, Xuan
AU - Brossard, Dominique
AU - Scheufele, Dietram A.
AU - Xenos, Michael A.
N1 - Publisher Copyright:
© 2016 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2017/3/4
Y1 - 2017/3/4
N2 - Recent technological developments have created novel opportunities for analyzing and identifying patterns in large volumes of digital content. However, many content analysis tools require researchers to choose between the validity of human-based coding and the ability to analyze large volumes of content through computer-based techniques. This study argues for the use of supervised content analysis tools that capitalize on the strengths of human- and computer-based coding for assessing opinion expression. We begin by outlining the key methodological issues surrounding content analysis as performed by human coders and existing computational algorithms. After reviewing the most popular analytic approaches, we introduce an alternative, hybrid method that is aimed at improving reliability, validity, and efficiency when analyzing social media content. To demonstrate the usefulness of this method, we track nuclear energy- and nanotechnology-related opinion expression on Twitter surrounding the Fukushima Daiichi accident to examine the extent to which the volume and tone of tweets shift in directions consistent with the expected external influence of the event. Our analysis revealed substantial shifts in both the volume and tone of nuclear power-related tweets that were consistent with our expectations following the disaster event. Conversely, there was decidedly more stability in the volume and tone of tweets for our comparison issue. These analyses provide an empirical demonstration of how the presented hybrid method can analyze defined communication sentiment and topics from large-scale social media data sets. The implications for communication scholars are discussed.
AB - Recent technological developments have created novel opportunities for analyzing and identifying patterns in large volumes of digital content. However, many content analysis tools require researchers to choose between the validity of human-based coding and the ability to analyze large volumes of content through computer-based techniques. This study argues for the use of supervised content analysis tools that capitalize on the strengths of human- and computer-based coding for assessing opinion expression. We begin by outlining the key methodological issues surrounding content analysis as performed by human coders and existing computational algorithms. After reviewing the most popular analytic approaches, we introduce an alternative, hybrid method that is aimed at improving reliability, validity, and efficiency when analyzing social media content. To demonstrate the usefulness of this method, we track nuclear energy- and nanotechnology-related opinion expression on Twitter surrounding the Fukushima Daiichi accident to examine the extent to which the volume and tone of tweets shift in directions consistent with the expected external influence of the event. Our analysis revealed substantial shifts in both the volume and tone of nuclear power-related tweets that were consistent with our expectations following the disaster event. Conversely, there was decidedly more stability in the volume and tone of tweets for our comparison issue. These analyses provide an empirical demonstration of how the presented hybrid method can analyze defined communication sentiment and topics from large-scale social media data sets. The implications for communication scholars are discussed.
KW - Content analysis
KW - Twitter
KW - computer-based coding
KW - human-based coding
KW - sentiment analysis
KW - supervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=84966710681&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84966710681&partnerID=8YFLogxK
U2 - 10.1080/1369118X.2016.1182197
DO - 10.1080/1369118X.2016.1182197
M3 - Article
AN - SCOPUS:84966710681
SN - 1369-118X
VL - 20
SP - 406
EP - 427
JO - Information Communication and Society
JF - Information Communication and Society
IS - 3
ER -