A computational model for mining consumer perceptions in social media

Topic Modeling as a Strategy of Inquiry in Organizational Research: A Tutorial With an Application Example on Organizational Culture

“…Further, organizational research may apply topic modeling to examine both job characteristics and competence profiles, similarly to how researchers in the information systems discipline have applied topic modeling (Gorbacheva, Stein, Schmiedel, & Müller, 2016; Müller, Schmiedel, Gorbacheva, & vom Brocke, 2014). In addition, organizational research may be inspired by applications in finance, where researchers used topic modeling to extract textual risk disclosures from annual reports to quantify their effect on the investors’ risk perceptions (Bao & Datta, 2014), and in the area of marketing and public relations, where researchers used topic modeling for mining consumer perceptions about brands from social media data (e.g., Pournarakis, Sotiropoulos, & Giaglis, 2017). Finally, organizational research may apply topic modeling for examining existing literature and analyzing the development of topics over time (e.g., Blei, 2012).…”

A multidisciplinary perspective of big data in management research

“…In addition, it is understudied in prior studies that what roles the firms play in the digital marketing campaign and their engagement in the online communication activities is an interesting area to be explored in future. Andrews et al(2015); Fong et al(2015); Ghose and Han(2014); Grewal et al(2016); Li and Du(2012); Luo et al(2014); Shankar et al(2016) Digital and social media advertising Ghose and Todri-Adamopoulos(2016); Gopinath et al(2013); Iyer and Katona(2015); Järvinen and Karjaluoto(2015); Kumar et al(2017); ; Liu and Mattila(2017); Malthouse et al(2013); Mariani et al(2016); Trusov et al(2016) Brand analysis Camiciottoli et al(2014); Culotta and Cutler(2016); Godey et al(2016); Gretry et al(2017); Hamilton et al(2016); Költringer and Dickinger(2015); Moro et al(2016); Nam and Kannan(2014); Nguyen et al(2015); Pournarakis et al(2017); Schweidel and Moe(2014); Tirunillai and Tellis(2014); Electronic word-of-mouth Balaji et al(2016); Cantallops and Salvi(2014); Cascio et al(2015); Chen and Xie (2005); Eisingerich et al(2015); Hennig-Thurau et al(2015); Kim et al(2015); King et al(2014); ; Lee and Song(2010); Mayzlin (2006); …”

Stakeholder sentiment in service supply chains: big data meets agenda-setting theory

“…In totality, sixty-seven CM documents (as defined by the inclusion of a firm representative) had 1408 social network posts. Data cleansing involved the elimination of non-English social network posts, non-letter characters, URLs, mentions and re-tweet identifiers (Pournarakis et al 2017 ). Overall a total of 508 social network posts were utilized for further analysis.…”

“…Data preparation for subsequent sentiment analysis for both the CM documents and the social network posts was based on the work of Sul et al (2017), who developed a model that combines topic and sentiment classification to elicit influential subjects from consumer perceptions in social media. Additionally, this study references Pournarakis et al ( 2017 ) who empirically apply a model to conduct an analysis of over 280,000 tweets related to a specific topic (i.e., Uber transportation network) with the goal of providing insights into awareness and meaning in brands. The authors introduce a model that evokes subjects from consumer perceptions in social media.…”

“…The authors introduce a model that evokes subjects from consumer perceptions in social media. As the purpose of this study examines a similar context (stakeholder perceptions of big data), without creating topic classifications, this study utilizes the data preparation and subsequent sentiment classification steps that Pournarakis et al ( 2017 ) define.…”