Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams

Lipizzi, Carlo; Iandoli, Luca; Marquez, Jose Emmanuel Ramirez

doi:10.1016/j.ijinfomgt.2015.04.001

Cited by 77 publications

(53 citation statements)

References 38 publications

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“…An explicit coding framework was not used because we wished to obtain a broad understanding of the Twitter discussions and profile information. Since Twitter content frequently contains abbreviations, contractions, and image representation of concepts (Lipizzi, Iandoli, & Ramirez Marquez, ), an a priori coding framework may have limited the amount of insight that could have been generated from the text.…”

Section: Methodsmentioning

confidence: 99%

Online Stakeholder Interactions in the Early Stage of a Megaproject

Williams

Ferdinand

Pasian

2015

Project Management Journal

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The purpose of this paper is to examine the network structure of online stakeholder discussions in the planning stage of a UK public mega project, High Speed Rail. By providing new rail connections between London, Birmingham and Manchester, this project is highly complex as it is embedded in a network of stakeholder relationships that may support or oppose the project.Data drawn from Twitter was analyzed using Social Network Analysis and inductive analysis of user profiles and content. Findings indicate that the majority of online stakeholders oppose the project and form stable clusters. Larger clusters within this network may attempt to deploy power directly in the form of a manipulation strategy while smaller clusters may seek to ally themselves with more powerful groups, a pathway strategy. Overall, the methodology is a useful complement to existing methods and may provide real time insights into the complex, evolving discussions around mega projects.

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Section: Methodsmentioning

confidence: 99%

Online Stakeholder Interactions in the Early Stage of a Megaproject

Williams

Ferdinand

Pasian

2015

Project Management Journal

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“…Social media data mining has become a common tool used to extract opinions from a large population in order to monitor, understand, and predict people's reactions to an event, and to measure the diffusion of ideas within the social network [15]. In this section, related work on extracting insights from collections of documents is presented.…”

Section: Standard Insights Extraction Pipelinementioning

confidence: 99%

“…The scope here is limited to social media data, specifically textual data, and more so, Twitter data, because the vast amount of content generated and shared through social media contains rich knowledge and covers a wide spectrum of social dynamics [33]. In their socio-semantic analysis of Twitter data, Lipizzi et al [15] stated that the following processes are necessary to extract complete and valuable insights from data: (1) preprocessing the text, (2) identifying and classifying opinions in the network, (3) analyzing the sentiment of individual or groups of text, (4) visualization of the large amounts of data; and (5) extracting conversational maps from social streams. We subdivided these processes into seven steps, including dimensionality reduction and clustering, shown in Fig.…”

Section: Standard Insights Extraction Pipelinementioning

confidence: 99%

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Evaluating the Effectiveness of the Standard Insights Extraction Pipeline for Bantu Languages

Nchabeleng

Byamugisha

2020

Lecture Notes in Computer Science

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Extracting insights from data obtained from the web in order to identify people's views and opinions on various topics is a growing practice. The standard insights extraction pipeline is typically an unsupervised machine learning task composed of processes that preprocess the text, visualize it, cluster and identify the topics and sentiment in each cluster, and then graph the network. Given the increasing amount of data being generated on the internet in Africa today, and the multilingual state of African countries, we evaluated how well the standard pipeline works when applied to text wholly or partially written in indigenous African languages, specifically Bantu languages. We carried out an exploratory investigation using Twitter data and compared the outputs from each step of the pipeline for an English dataset and a mixed Bantu language dataset. We found that for Bantu languages, due to their complex grammatical structure, extra preprocessing steps such as part-of-speech tagging and morphological analysis are required during data cleaning, threshold values should be adjusted during topic modeling, and semantic analysis should be performed before completing text preprocessing.

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“…Such online "Big Data" have the potential to tap into collective patterns of perceptions, attitudes, behaviours and social structures in ways that other sources cannot (George et al, 2014;Hannigan, 2015). Consequently, online data have been used to gauge various topics ranging from stock prices and political opinion to consumer reactions and supply chain analytics (Bollen et al, 2011;Chae, 2015;Lipizzi et al, 2015;Sobkowicz et al, 2012;Tetlock, 2007), and as an emerging decision-making tool both in the business community and for policy makers. Overall, online "Big Data" are evolving rapidly and at a speed that leaves scholars and practitioners alike attempting to make sense of its potential opportunities and risks (George et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Using online data and network-based text analysis in HRM research

Platanou

Mäkelä

Beletskiy

et al. 2017

JOEPP

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Purpose The purpose of this paper is to propose new directions for human resource management (HRM) research by drawing attention to online data as a complementary data source to traditional quantitative and qualitative data, and introducing network text analysis as a method for large quantities of textual material. Design/methodology/approach The paper first presents the added value and potential challenges of utilising online data in HRM research, and then proposes a four-step process for analysing online data with network text analysis. Findings Online data represent a naturally occuring source of real-time behavioural data that do not suffer from researcher intervention or hindsight bias. The authors argue that as such, this type of data provides a promising yet currently largely untapped empirical context for HRM research that is particularly suited for examining discourses and behavioural and social patterns over time. Practical implications While online data hold promise for many novel research questions, it is less appropriate for research questions that seek to establish causality between variables. When using online data, particular attention must be paid to ethical considerations, as well as the validity and representativeness of the sample. Originality/value The authors introduce online data and network text analysis as a new avenue for HRM research, with potential to address novel research questions at micro-, meso- and macro-levels of analysis.

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Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams

Cited by 77 publications

References 38 publications

Online Stakeholder Interactions in the Early Stage of a Megaproject

Online Stakeholder Interactions in the Early Stage of a Megaproject

Evaluating the Effectiveness of the Standard Insights Extraction Pipeline for Bantu Languages

Using online data and network-based text analysis in HRM research

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