A Deep Learning Semantic Approach to Emotion Recognition Using the IBM Watson Bluemix Alchemy Language

Biondi, Giulio; Franzoni, Valentina; Poggioni, Valentina

doi:10.1007/978-3-319-62398-6_51

Cited by 33 publications

(17 citation statements)

References 22 publications

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“…For example, AI can analyze vast data sets of written and non-written user-generated content on social media platforms which can reveal insights to B2B marketers about user needs, preferences, attitudes and behaviors (Martínez et al , 2016). The AI system IBM Watson, for example, has the capabilities to identify sentiment, emotions, values and attitudes expressed in a piece of text (Biondi et al , 2017; IBM, 2018). These psychographic characteristics can be a valuable source of insight for B2B marketers for innovation and new product development efforts.…”

Section: Implications Of Artificial Intelligence For Market Knowledge In B2b Marketingmentioning

confidence: 99%

Artificial intelligence (AI) and its implications for market knowledge in B2B marketing

Paschen

Kietzmann

2019

JBIM

228

183

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Purpose The purpose of this paper is to explain the technological phenomenon artificial intelligence (AI) and how it can contribute to knowledge-based marketing in B2B. Specifically, this paper describes the foundational building blocks of any artificial intelligence system and their interrelationships. This paper also discusses the implications of the different building blocks with respect to market knowledge in B2B marketing and outlines avenues for future research. Design/methodology/approach The paper is conceptual and proposes a framework to explicate the phenomenon AI and its building blocks. It further provides a structured discussion of how AI can contribute to different types of market knowledge critical for B2B marketing: customer knowledge, user knowledge and external market knowledge. Findings The paper explains AI from an input–processes–output lens and explicates the six foundational building blocks of any AI system. It also discussed how the combination of the building blocks transforms data into information and knowledge. Practical implications Aimed at general marketing executives, rather than AI specialists, this paper explains the phenomenon artificial intelligence, how it works and its relevance for the knowledge-based marketing in B2B firms. The paper highlights illustrative use cases to show how AI can impact B2B marketing functions. Originality/value The study conceptualizes the technological phenomenon artificial intelligence from a knowledge management perspective and contributes to the literature on knowledge management in the era of big data. It addresses calls for more scholarly research on AI and B2B marketing.

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Section: Implications Of Artificial Intelligence For Market Knowledge In B2b Marketingmentioning

confidence: 99%

Artificial intelligence (AI) and its implications for market knowledge in B2B marketing

Paschen

Kietzmann

2019

JBIM

228

183

View full text Add to dashboard Cite

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“…The use of Neural Network (NN) models has been steadily increasing in the recent past, following the introduction of Deep Learning methods and the ever-growing computational capabilities of modern machines. Thus, such models are applied to various problems, including image classification [1] and generation [2], text classification [3], speech recognition [4], emotion recognition [5], and many more. New and more complex network structures, such as Convolutional Neural Networks [6], Neural Turing Machines [7], and NRAM [8], were developed and applied to the aforementioned tasks; such new problems and structures also required the development of new optimization techniques [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution for Neural Networks Optimization

et al. 2020

Self Cite

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In this paper, a Neural Networks optimizer based on Self-adaptive Differential Evolution is presented. This optimizer applies mutation and crossover operators in a new way, taking into account the structure of the network according to a per layer strategy. Moreover, a new crossover called interm is proposed, and a new self-adaptive version of DE called MAB-ShaDE is suggested to reduce the number of parameters. The framework has been tested on some well-known classification problems and a comparative study on the various combinations of self-adaptive methods, mutation, and crossover operators available in literature is performed. Experimental results show that DENN reaches good performances in terms of accuracy, better than or at least comparable with those obtained by backpropagation.

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“…We used a students' total posts per week for behavioural engagement (i.e., a fre- Oberlander & Gill, 2004;Pennebaker & King, 1999). The tool uses a psycholinguistic dictionary of textual cues that can signal underlying emotions in writing (e.g., the Linguistic Inquiry and Word Count; Pennebaker, Francis, & Booth, 2001;Tausczik & Pennebaker, 2010) and grows in accuracy through machine learning (Biondi, Franzoni, & Poggioni, 2017;Mostafa, Crick, Calderon, & Oatley, 2016;Wang & Pal, 2015). Specifically, the IBM tone analyser generates probabilities for five discrete emotions: anger, disgust, fear, sadness and joy.…”

Section: Learner Engagementmentioning

confidence: 99%

How students emerge as learning leaders in small group online discussions

Kim

Lee

Wang

2020

Computer Assisted Learning

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In this study, we examined the role of leadership styles and multi‐dimensional learner engagement in how students emerge as learning leaders in asynchronous online discussions. Grounded in the conceptual framework of two dominant leadership styles of transformational and transactional leadership, this study applies the two leadership styles—transformational leadership and transactional leadership—to the Leader Identification Method (LIM) which defines three types of leader roles (i.e., full, transactional and attractive facilitator) in online learning. We collected data from 20 students enrolled in a graduate‐level online course that required participation in 12‐week discussion activities. Results of the longitudinal data analyses show that person‐focused, transformational leadership and active participation in online discussions were significant factors that enabled students to emerge as learning leaders. Students are more likely to become leaders by exhibiting transformational leadership behaviour and productively interacting with one another in an online discussion community.

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A Deep Learning Semantic Approach to Emotion Recognition Using the IBM Watson Bluemix Alchemy Language

Cited by 33 publications

References 22 publications

Artificial intelligence (AI) and its implications for market knowledge in B2B marketing

Artificial intelligence (AI) and its implications for market knowledge in B2B marketing

Differential Evolution for Neural Networks Optimization

How students emerge as learning leaders in small group online discussions

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