Value co-creation for open innovation: An evidence-based study of the data driven paradigm of social media using machine learning.

Adikari, Achini; Burnett, Donna; Sedera, Darshana; Silva, Daswin De; Alahakoon, Damminda

doi:10.1016/j.jjimei.2021.100022

Cited by 39 publications

(28 citation statements)

References 48 publications

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“…So, the big data about discussion or the top trend by the user arguments we analyzed. A similar technique was used by ( Adikari et al., 2021 ) to “detect the public emotions.” ( Huang et al., 2022 ) used this technique for online reviews. Fig.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

Technology & behavioral changes mediation for personnel safety intentions: Crisis in theoretical framework.

Nasir¹,

Ausaf²,

Shi³

et al. 2022

International Journal of Information Management Data Insights

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Section: Data Analysis and Resultsmentioning

confidence: 99%

Technology & behavioral changes mediation for personnel safety intentions: Crisis in theoretical framework.

Nasir¹,

Ausaf²,

Shi³

et al. 2022

International Journal of Information Management Data Insights

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“…The justification for the use of this methodology can be found in the work of authors such as Adikari et al (2021) who propose a machine learning approach that can be used to transform social media data into actionable insights. Adikari et al (2021) address an evidence-based study that uses machine learning algorithms to generate actionable insights of strategic value from a data-driven paradigm. These outcomes provide fresh perspectives and new thinking that advances social media as an emergent information asset for end-to-end open innovation and incremental value co-creation.…”

Section: Discussionmentioning

confidence: 99%

Perceptions and Attitudes of Spanish “Digital Seniors” Towards E-Government

Valle

Barroso

Alcalá

2022

International Journal of Electronic Government Research

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A typology of user was explored as to behaviour and perception of e-Government through a telephone survey involving 405 Internet users between 60 and 79 years of age. Ten different groups were identified by means of dimensionality reduction techniques followed by a cluster analysis. The main conclusion is that there is no consistent pattern, although among the groups which do use e-Government, they have less concerns than the rest and see less need for modifications to be applied to adapt the websites to the needs of seniors. Within this older age group, two types are clearly identified. Those who feel uncomfortable, which explains their avoidance of e-Government, and to a lesser degree, those whose lack of contact with public administration may be due to a lack of interest for such services. It appears that a senior-friendly offering together with learning opportunities would increase interest for this channel.

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“…Emotional intelligence (or an artificial counterpart) is important for machines when interacting with humans, as emotions and the ability to sense emotion play an important role in maintaining productive social interactions [35]. A significant volume of research has been conducted in the area of detection of emotionally relevant information from different sources, offering positive as well as compelling evidence of impact on multiple fields covering healthcare, human resource management and Artificial Intelligence [1][2][3][4]6]. The traditional…”

Section: Conceptual Backgroundmentioning

confidence: 99%

“…Second, alongside the BoAW feature embeddings, we propose an appropriate attention mechanism that best aligns with the feature representations input to the emotion detection model. 3. Third, we explore and evaluate the robustness and the effectiveness of this feature extraction and embedding process followed by an emotion detection model which utilizes conversation context information for emotion class predictions.…”

Section: Introductionmentioning

confidence: 99%

A voice-based real-time emotion detection technique using recurrent neural network empowered feature modelling

Chamishka

Madhavi

Nawaratne

et al. 2022

Multimed Tools Appl

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The advancements of the Internet of Things (IoT) and voice-based multimedia applications have resulted in the generation of big data consisting of patterns, trends and associations capturing and representing many features of human behaviour. The latent representations of many aspects and the basis of human behaviour is naturally embedded within the expression of emotions found in human speech. This signifies the importance of mining audio data collected from human conversations for extracting human emotion. Ability to capture and represent human emotions will be an important feature in next-generation artificial intelligence, with the expectation of closer interaction with humans. Although the textual representations of human conversations have shown promising results for the extraction of emotions, the acoustic feature-based emotion detection from audio still lags behind in terms of accuracy. This paper proposes a novel approach for feature extraction consisting of Bag-of-Audio-Words (BoAW) based feature embeddings for conversational audio data. A Recurrent Neural Network (RNN) based state-of-the-art emotion detection model is proposed that captures the conversation-context and individual party states when making real-time categorical emotion predictions. The performance of the proposed approach and the model is evaluated using two benchmark datasets along with an empirical evaluation on real-time prediction capability. The proposed approach reported 60.87% weighted accuracy and 60.97% unweighted accuracy for six basic emotions for IEMOCAP dataset, significantly outperforming current state-of-the-art models.

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Value co-creation for open innovation: An evidence-based study of the data driven paradigm of social media using machine learning.

Cited by 39 publications

References 48 publications

Technology & behavioral changes mediation for personnel safety intentions: Crisis in theoretical framework.

Technology & behavioral changes mediation for personnel safety intentions: Crisis in theoretical framework.

Perceptions and Attitudes of Spanish “Digital Seniors” Towards E-Government

A voice-based real-time emotion detection technique using recurrent neural network empowered feature modelling

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