Modelling Influence and Reach in Sentiment Analysis

Neves-Silva, Rui; Gamito, Marta; Pina, Paulo; Campos, Ana Rita

doi:10.1016/j.procir.2016.03.239

Cited by 11 publications

(13 citation statements)

References 19 publications

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“…This ends the cycle of feedback acquisition. Reach is then used to provide a weighted measure of the overall sentiment towards the PSS [12]. For the purpose of monitoring opinions, external or internal stakeholders are not differentiated.…”

Section: Figure 2 -Sentiment Analysis Architecturementioning

confidence: 99%

“…This is a differentiating aspect when compared to other opinion mining tools in the market, which are more focused on marketing and brands, instead of the feedback for design purposes. It also has unique characteristics in its use of reach and influence from elements of electronic word-of-mouth to weigh the relative importance of each opinion to the overall sentiment index and for its focus on the specificities of PSS [12].…”

Section: B Non-standard Feedback Toolsmentioning

confidence: 99%

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A sentiment analysis solution for the re-design of product-service systems

Pina

Gois

Mano

et al. 2017

2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)

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In an extremely competitive economy, companies need to get ahead of competition as fast as possible, both in delivering quality of products and services, but also in extending their lifecycle to match the expectations and needs of customers. Analysing where product and service systems are lacking in terms of customer requirements is crucial. Currently it might take some time for information to travel from customer to producer, since the connection may include stores and local representatives before reaching the products' and services' designers. Although this information is readily available in social networks, the issue resides in efficiently merging and showing it in a simple and meaningful way to the designer of new products and systems. By identifying important parameters in posts and opinions, data becomes easier to qualify and, as a result, easier to identify by a designer. In this document, we describe a solution for this problem.

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Section: Figure 2 -Sentiment Analysis Architecturementioning

confidence: 99%

Section: B Non-standard Feedback Toolsmentioning

confidence: 99%

A sentiment analysis solution for the re-design of product-service systems

Pina

Gois

Mano

et al. 2017

2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)

Self Cite

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“…Sentiment analysis is used to acquire feedback from endusers and stakeholders across the lifecycle of the PSS [6] [7]. This feedback provides the designers with an insight into the general sentiment from users towards a PSS, as well as predicting the evolution of the sentiment based on sentiment trends of similar PSSs.…”

Section: F Sentiment Analysismentioning

confidence: 99%

Engineering environment to support product-service design using value chain data

Campos

Correia

Mourtzis

et al. 2017

2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)

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Companies are looking for new approaches and tools to help them meet their customer's demands, particularly the desire of customized products at lower costs. The paradigm of product-service systems brings companies the opportunity of enlarging their offer, looking at products and services in an integrated way. This paper presents the early prototype of a software platform that supports companies in designing PSSs using feedback information collected over the value chain. The prototype described has been tested in three industrial companies and is currently being further developed.

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“…A post may be much participated, but its author may not have a significant audience or vice-versa. The point is that opinions of authors whose posts consistently have a high reach have a greater influence in the overall sentiment towards a product [2].…”

Section: First Of All What Is Social Media?mentioning

confidence: 99%

“…The feedback is collected in the format of posts (i.e. plain text) and processed to extract the corresponding sentiment [2]. For that purpose, the DIVERSITY platform includes a Sentiment Analysis component that continuously receives posts that reflect some type of opinion about a specific product (or PSS) and uses natural language processing to quantify the degree of satisfaction, i.e.…”

Section: Introductionmentioning

confidence: 99%

OpinionSim: Modelling and simulating stakeholders' feedback to product-service systems

Neves-Silva

Pina

2017

2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)

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This paper describes the OpinionSim simulator developed in the scope of project DIVERSITY. DIVERSITY is related with the design of product-service systems (PSS) using real time feedback loops from internal and external stakeholders. For that purpose, the DIVERSITY platform includes Sentiment Analysis components that process posts retrieved from social media networks. Nonetheless, the test of Sentiment Analysis components requires a dataset, i.e. a set of posts, which exploit the full range of possible opinions in a controlled way. Additionally, to test the market segmentation functionalities we need to control the characteristics of the population generating the posts. The OpinionSim simulator here described meets the requirements to support the testing process.

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Modelling Influence and Reach in Sentiment Analysis

Cited by 11 publications

References 19 publications

A sentiment analysis solution for the re-design of product-service systems

A sentiment analysis solution for the re-design of product-service systems

Engineering environment to support product-service design using value chain data

OpinionSim: Modelling and simulating stakeholders' feedback to product-service systems

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