scholarly journals Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1322
Author(s):  
Wilfredo Graterol ◽  
Jose Diaz-Amado ◽  
Yudith Cardinale ◽  
Irvin Dongo ◽  
Edmundo Lopes-Silva ◽  
...  
Keyword(s):  
Language Processing ◽  
Application Programming Interface ◽  
Social Robots ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Emotional States ◽  
Emotion Detection ◽  
Learning Techniques ◽  
Wide Range ◽  
Application Programming

For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption. Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement.

scholarly journals Competitor Identification by Use The Sentiment Classification Based on the User Research

2019 ◽  
Vol 8 (9S2) ◽  
pp. 871-874
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
The Other ◽  
Machine Learning Techniques ◽  
Single Product ◽  
User Research ◽  
Customer Reviews ◽  
Learning Techniques ◽  
Wide Range

Online shopping's have achieved an immense growth. All like to do it as there is no need to physically to the shop and we have a wide range of collections available in the online sites from which we can actually buy the product. The customers usually tend to purchase a product that has a good customer review and has the highest rating. Numerous reviews are given for a single product and the most of the important reviews are not organized well which makes it disappear from the other reviews. Numerous researchers have worked on structuring the reviews for various purposes. In this work we propose a sentimental analysis of customer reviews for various hotel items. All the items are reviewed by the customers and the proposed work makes an analysis of the reviews obtained for a particular item in all the available shops. This analysis is helpful injudging the most likely consumed food by the customers around and can get to know the competiveness of the product being delivered to the customers. Machine Learning techniques and Natural language Processing (NLP) are used for the proposed work and is observed to produce an efficient result.

scholarly journals A Survey on Twitter Sentimental Analysis with Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 462
Author(s):  
G Krishna Chaitanya ◽  
Dinesh Reddy Meka ◽  
Vakalapudi Surya Vamsi ◽  
M V S Ravi Karthik
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Buying Behavior ◽  
Online Retail ◽  
User Reviews ◽  
Business Plans ◽  
Learning Techniques ◽  
User Data

Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general. 

scholarly journals Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Majid Amirfakhrian ◽  
Mahboub Parhizkar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Machine Vision ◽  
Traffic Accidents ◽  
Fuzzy Theory ◽  
Machine Learning Techniques ◽  
Industrial Setting ◽  
Technological Advances ◽  
Learning Techniques ◽  
Wide Range

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.

scholarly journals DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO PREDICT AUTHOR’S AGE FROM TEXT

2020 ◽  
Vol 7 (10) ◽  
pp. 380-389
Author(s):  
Asogwa D.C ◽  
Anigbogu S.O ◽  
Anigbogu G.N ◽  
Efozia F.N
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Age Group ◽  
Political Views ◽  
Learning Techniques ◽  
Age Prediction

Author's age prediction is the task of determining the author's age by studying the texts written by them. The prediction of author’s age can be enlightening about the different trends, opinions social and political views of an age group. Marketers always use this to encourage a product or a service to an age group following their conveyed interests and opinions. Methodologies in natural language processing have made it possible to predict author’s age from text by examining the variation of linguistic characteristics. Also, many machine learning algorithms have been used in author’s age prediction. However, in social networks, computational linguists are challenged with numerous issues just as machine learning techniques are performance driven with its own challenges in realistic scenarios. This work developed a model that can predict author's age from text with a machine learning algorithm (Naïve Bayes) using three types of features namely, content based, style based and topic based. The trained model gave a prediction accuracy of 80%.

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