scholarly journals A Novel Approach of Integrating Natural Language Processing Techniques with Fuzzy TOPSIS for Product Evaluation

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 120
Author(s):  
Tianxiong Wang
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Product Design ◽  
Language Processing ◽  
Evaluation Method ◽  
Product Evaluation ◽  
Fuzzy Topsis ◽  
Practical Significance ◽  
Processing Techniques ◽  
Product Forms

Product designers need to fully understand consumers’ emotional preferences and responses for product forms to improve products. However, users and designers have different understandings and concepts in the product evaluation process, which will lead to cognitive asymmetry in the product design and evaluating process. This phenomenon prevents designers to grasp users’ needs, increasing the risk of product development failure. To this end, this paper proposes a product evaluation method that combines natural language processing techniques and fuzzy multi-criteria decision-making into a new integrated way to reduce the cognitive difference between users and designers, so as to solve the problem of cognitive asymmetry. This was done firstly by obtaining the review data of products from users on the Internet, based on a web crawler, and then constructing word vectors based on natural language processing techniques to realize the parametric expression of the Kansei image. Secondly, by using a statistical method to extract the product scheme that meets the preferences of users and designers, and then quantifying the relationship between the product form and Kansei image based on a grey relational analysis (GRA). Finally, by calculating the indicator weight based on the Entropy method and using the fuzzy TOPSIS method to explore the prioritization of the product design alternatives in view of the Kansei needs of users. Taking the smart capsule coffee machine as an example, the feasibility and effectiveness of this method are verified. In particular, the method proposed in this research can not only enable different cognitive subjects to achieve cognitive symmetry, but also filter out product forms that meet the cognitive needs of users. Moreover, this study provides a theoretical basis and practical significance for reducing the cognitive differences between cognitive subjects in the whole process of product design, and provides a systematic framework for the industry to effectively connect customer needs and product design decisions. At the same time, this study has introduced a new method for Kansei engineering.

scholarly journals A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

Identifying and intercepting health misinformation on Reddit dermatology forums with artificially intelligent bots using natural language processing (Preprint)

2021 ◽  
Author(s):  
Monique B. Sager ◽  
Aditya M. Kashyap ◽  
Mila Tamminga ◽  
Sadhana Ravoori ◽  
Christopher Callison-Burch ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
The United States ◽  
Test Accuracy ◽  
Limited Data ◽  
Test Environment ◽  
Data Set ◽  
Inappropriate Care ◽  
Processing Techniques

BACKGROUND Reddit, the fifth most popular website in the United States, boasts a large and engaged user base on its dermatology forums where users crowdsource free medical opinions. Unfortunately, much of the advice provided is unvalidated and could lead to inappropriate care. Initial testing has shown that artificially intelligent bots can detect misinformation on Reddit forums and may be able to produce responses to posts containing misinformation. OBJECTIVE To analyze the ability of bots to find and respond to health misinformation on Reddit’s dermatology forums in a controlled test environment. METHODS Using natural language processing techniques, we trained bots to target misinformation using relevant keywords and to post pre-fabricated responses. By evaluating different model architectures across a held-out test set, we compared performances. RESULTS Our models yielded data test accuracies ranging from 95%-100%, with a BERT fine-tuned model resulting in the highest level of test accuracy. Bots were then able to post corrective pre-fabricated responses to misinformation. CONCLUSIONS Using a limited data set, bots had near-perfect ability to detect these examples of health misinformation within Reddit dermatology forums. Given that these bots can then post pre-fabricated responses, this technique may allow for interception of misinformation. Providing correct information, even instantly, however, does not mean users will be receptive or find such interventions persuasive. Further work should investigate this strategy’s effectiveness to inform future deployment of bots as a technique in combating health misinformation. CLINICALTRIAL N/A

scholarly journals Recent Advances in Conversational Intelligent Tutoring Systems

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 42-54 ◽  
Author(s):  
Vasile Rus ◽  
Sidney D’Mello ◽  
Xiangen Hu ◽  
Arthur Graesser
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Individual Student ◽  
Learning Progressions ◽  
Tutoring Systems ◽  
Recent Advances ◽  
Processing Techniques

We report recent advances in intelligent tutoring systems with conversational dialogue. We highlight progress in terms of macro and microadaptivity. Macroadaptivity refers to a system’s capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system’s capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue and natural language processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language processing techniques are key features of DeepTutor, the first intelligent tutoring system based on learning progressions. These improvements extend the bandwidth of possibilities for tailoring instruction to each individual student which is needed for maximizing engagement and ultimately learning.

Applying Natural Language Processing Techniques to Generate Open Data Web APIs Documentation

2020 ◽  
pp. 416-432
Author(s):  
César González-Mora ◽  
Cristina Barros ◽  
Irene Garrigós ◽  
Jose Zubcoff ◽  
Elena Lloret ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Open Data ◽  
Processing Techniques

Natural-language processing and automatic indexing

1990 ◽  
Vol 17 (1) ◽  
pp. 21-29
Author(s):  
C. Korycinski ◽  
Alan F. Newell
Keyword(s):  
Natural Language Processing ◽  
Statistical Analysis ◽  
Natural Language ◽  
Language Processing ◽  
Database Systems ◽  
Statistical Techniques ◽  
Free Text ◽  
Automatic Indexing ◽  
Text Database ◽  
Processing Techniques

The task of producing satisfactory indexes by automatic means has been tackled on two fronts: by statistical analysis of text and by attempting content analysis of the text in much the same way as a human indexcr does. Though statistical techniques have a lot to offer for free-text database systems, neither method has had much success with back-of-the-bopk indexing. This review examines some problems associated with the application of natural-language processing techniques to book texts.

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