Analysing discussions in social networks using group decision making methods and sentiment analysis

Morente-Molinera, Juan Antonio; Kou, Gang; Peng, Yi; Albero, Cristóbal Torres; Herrera‐Viedma, Enrique

doi:10.1016/j.ins.2018.03.020

Cited by 73 publications

(26 citation statements)

References 33 publications

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“…Sentiment, which expresses the underlying attitude and character of a fundraiser, is an essential factor affecting funders (Wang et al 2017;Pengnate and Riggins 2020;Morente-Molinera et al 2018. Positive sentiment could increase the success rate of fundraising (Wang et al 2017;Jiang et al 2020).…”

Section: Narratives and Sentiment In Crowdfundingmentioning

confidence: 99%

How text sentiment moderates the impact of motivational cues on crowdfunding campaigns

et al. 2021

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Arousing funders’ motives for crowdfunding participation is a challenge for every entrepreneur. According to self-determination theory, crowdfunding involvement is driven by two kinds of motivations: extrinsic and intrinsic motives. Campaign narratives with different sentimental orientations could act as cues triggering funders’ motives of capital-giving differently. Thus, we focus on the moderating effect of text sentiment on the motivational cues in crowdfunding campaigns. We hypothesize that the sentiment of campaign narratives moderates the relationship between motivational cues and fundraising success. Data are selected from 270,000 campaigns of the crowdfunding website Indiegogo. Empirical analysis demonstrates that the sentiment strength positively moderates the effect of intrinsic motives on fundraising success but negatively moderates the impact of extrinsic motives on fundraising success. The findings provide managerial insights for practitioners to stimulate specified motives by using the narratives with proper sentiment strength.

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Section: Narratives and Sentiment In Crowdfundingmentioning

confidence: 99%

How text sentiment moderates the impact of motivational cues on crowdfunding campaigns

et al. 2021

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“…Morente-Molinera et al [22] presented a method to determine how debate on the social network is progressing based on the consensus among the participants. Sentiment analysis was also used to measure the preference level that social media users have regarding a specific set of alternatives.…”

Section: Related Workmentioning

confidence: 99%

Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †

et al. 2020

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Influencer marketing is a modern method that uses influential users to approach goal customers easily and quickly. An online social network is a useful platform to detect the most effective influencer for a brand. Thus, we have an issue: how can we extract user data to determine an influencer? In this paper, a model for representing a social network based on users, tags, and the relationships among them, called the SNet model, is presented. A graph-based approach for computing the impact of users and the speed of information propagation, and measuring the favorite brand of a user and sharing the similar brand characteristics, called a passion point, is proposed. Therefore, we consider two main influential measures, including the extent of the influence on other people by the relationships between users and the concern to user’s tags, and the tag propagation through social pulse on the social network. Based on these, the problem of determining the influencer of a specific brand on a social network is solved. The results of this method are used to run the influencer marketing strategy in practice and have obtained positive results.

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“…It will be of interest to analyze whether this kind of opinion dynamics can lead to the definition of a collective decision model and see if the satisfaction or the power measures still provide good measures of leadership. Under this approach, it is also interesting to devise decision models, and the adequate measures for leadership, based on well established group decision dynamics, discrete or continuous, having multiple opinion dynamics orGand multicriteria decision making, like the ones considered in [22,21,20,39,1,28].…”

Section: Conclusion and Open Problemsmentioning

confidence: 99%

Measuring satisfaction and power in influence based decision systems

Molinero

Riquelme

Serna

2019

Knowledge-Based Systems

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Analysing discussions in social networks using group decision making methods and sentiment analysis

Cited by 73 publications

References 33 publications

How text sentiment moderates the impact of motivational cues on crowdfunding campaigns

How text sentiment moderates the impact of motivational cues on crowdfunding campaigns

Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †

Measuring satisfaction and power in influence based decision systems

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