scholarly journals KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

2022 ◽  
Author(s):  
Giuseppe D’Aniello ◽  
Matteo Gaeta ◽  
Ilaria La Rocca
Keyword(s):  
Sentiment Analysis ◽  
Reference Model ◽  
State Of The Art ◽  
Qualitative Case Study ◽  
Product Reviews ◽  
Current Trends ◽  
Critical Issues ◽  
Novel Approaches ◽  
Basic Concepts

AbstractThe analysis of the opinions of customers and users has been always of great interest in supporting decision-making in many fields, especially in marketing. Sentiment analysis (SA) is the umbrella term for techniques and approaches that analyze user’s sentiments, emotions, opinions in text or other media. The need for a better understanding of these opinions paved the way to novel approaches that focus on the analysis of the sentiment related to specific features of a product, giving birth to the field of aspect-based sentiment analysis (ABSA). Although the increasing interest in this discipline, there is still confusion regarding the basic concepts of ABSA: terms like sentiment, affect, emotion, opinion, are used as synonyms while they represent different concepts. This often leads to an incorrect analysis of the users’ opinions.This work presents an overview of the state-of-the-art techniques and approaches for ABSA, highlighting the main critical issues related to current trends in this field. Following this analysis, a new reference model for SA and ABSA, namely the KnowMIS-ABSA model, is proposed. The model is grounded on the consideration that sentiment, affect, emotion and opinion are very different concepts and that it is profoundly wrong to use the same metric and the same technique to measure them. Accordingly, we argue that different tools and metrics should be adopted to measure each of the dimensions of an opinion. A qualitative case study, regarding product reviews, is proposed to motivate the advantages of the KnowMIS-ABSA model.

scholarly journals Arabic dialect sentiment analysis with ZERO effort. \\ Case study: Algerian dialect

2020 ◽  
Vol 23 (65) ◽  
pp. 124-135
Author(s):  
Imane Guellil ◽  
Marcelo Mendoza ◽  
Faical Azouaou
Keyword(s):  
Sentiment Analysis ◽  
Short Term Memory ◽  
State Of The Art ◽  
Short Term ◽  
Term Memory ◽  
Ongoing Work ◽  
Long Short Term Memory ◽  
Large Corpus ◽  
Unique Condition

This paper presents an analytic study showing that it is entirely possible to analyze the sentiment of an Arabic dialect without constructing any resources. The idea of this work is to use the resources dedicated to a given dialect \textit{X} for analyzing the sentiment of another dialect \textit{Y}. The unique condition is to have \textit{X} and \textit{Y} in the same category of dialects. We apply this idea on Algerian dialect, which is a Maghrebi Arabic dialect that suffers from limited available tools and other handling resources required for automatic sentiment analysis. To do this analysis, we rely on Maghrebi dialect resources and two manually annotated sentiment corpus for respectively Tunisian and Moroccan dialect. We also use a large corpus for Maghrebi dialect. We use a state-of-the-art system and propose a new deep learning architecture for automatically classify the sentiment of Arabic dialect (Algerian dialect). Experimental results show that F1-score is up to 83% and it is achieved by Multilayer Perceptron (MLP) with Tunisian corpus and with Long short-term memory (LSTM) with the combination of Tunisian and Moroccan. An improvement of 15% compared to its closest competitor was observed through this study. Ongoing work is aimed at manually constructing an annotated sentiment corpus for Algerian dialect and comparing the results

scholarly journals Methodology for integrating artificial intelligence in healthcare systems: learning from COVID-19 to prepare for Disease X

AI and Ethics ◽  
2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Carsten Maple ◽  
Uchenna Ani
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
Qualitative Case Study ◽  
Healthcare Systems ◽  
Lessons Learned ◽  
Case Study Method ◽  
Research Problems ◽  
Systems Learning ◽  
Digital Healthcare

AbstractArtificial intelligence and edge devices have been used at an increased rate in managing the COVID-19 pandemic. In this article we review the lessons learned from COVID-19 to postulate possible solutions for a Disease X event. The overall purpose of the study and the research problems investigated is the integration of artificial intelligence function in digital healthcare systems. The basic design of the study includes a systematic state-of-the-art review, followed by an evaluation of different approaches to managing global pandemics. The study design then engages with constructing a new methodology for integrating algorithms in healthcare systems, followed by analysis of the new methodology and a discussion. Action research is applied to review existing state of the art, and a qualitative case study method is used to analyse the knowledge acquired from the COVID-19 pandemic. Major trends found as a result of the study derive from the synthesis of COVID-19 knowledge, presenting new insights in the form of a conceptual methodology—that includes six phases for managing a future Disease X event, resulting with a summary map of various problems, solutions and expected results from integrating functional AI in healthcare systems.

Investigating Machine Learning Techniques for User Sentiment Analysis

2019 ◽  
Vol 11 (3) ◽  
pp. 1-12 ◽  
Author(s):  
Nimesh V Patel ◽  
Hitesh Chhinkaniwala
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Product Reviews ◽  
Learning Approaches ◽  
Current Trends ◽  
Learning Techniques ◽  
Benchmark Datasets

Sentiment analysis identifies users in the textual reviews available in social networking sites, tweets, blog posts, forums, status updates to share their emotions or reviews and these reviews are to be used by market researchers to do know the product reviews and current trends in the market. The sentiment analysis is performed by two methods. Machine learning approaches and lexicon methods which are also known as the knowledge base approach. These. In this article, the authors evaluate the performance of some machine learning techniques: Maximum Entropy, Naïve Bayes and Support Vector Machines on two benchmark datasets: the positive-negative dataset and a Movie Review dataset by measuring parameters like accuracy, precision, recall and F-score. In this article, the authors present the performance of various sentiment analysis and classification methods by classifying the reviews in binary classes as positive, negative opinion about reviews on different domains of dataset. It is also justified that sentiment analysis using the Support Vector Machine outperforms other machine learning techniques.

scholarly journals The role of communicative acts in the Dream process: engaging Moroccan migrants in a community development initiative in urban Spain

2017 ◽  
Vol 54 (2) ◽  
pp. 197-214 ◽  
Author(s):  
Anna Carrillo ◽  
Sandra Girbés-Peco ◽  
Lena De Botton ◽  
Rosa Valls-Carol
Keyword(s):  
Community Development ◽  
State Of The Art ◽  
Positive Impact ◽  
Qualitative Case Study ◽  
Case Study Research ◽  
Living Conditions ◽  
Communicative Acts ◽  
Development Processes

Abstract The present article offers relevant insights into how the evidence-based community development initiative known as the Dream process has had a positive impact on the inclusion, participation and leadership of a marginalized community of Moroccan immigrants in urban Spain. More specifically, we analyse how the commitment to promote dialogic communicative acts and to reduce power communicative acts during the process has attenuated some of the race, gender and class barriers that hindered the community’s involvement in dialogic and decision-making spaces aimed at improving their living conditions. In this article, we first introduce the state of the art using studies that have examined the role of interaction and deliberation in community development processes in disadvantaged contexts. Then, we briefly refer to the deterioration of the living conditions of the Moroccan immigrant population in Spain. Finally, we present the main results obtained from the qualitative case study research carried out through the implementation of the communicative methodology. This case study provides both theoretical claims and practical orientations to examine how dialogic approaches can contribute to community development processes in contexts severely affected by racial segregation and poverty.

Intelligent product redesign strategy with ontology-based fine-grained sentiment analysis

2021 ◽  
pp. 1-21
Author(s):  
Siyu Zhu ◽  
Jin Qi ◽  
Jie Hu ◽  
Haiqing Huang
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
House Of Quality ◽  
Customer Preference ◽  
Fine Grained ◽  
Customer Preferences ◽  
Analysis Methods ◽  
System Information ◽  
Increasing Demand

Abstract With the increasing demand for a personalized product and rapid market response, many companies expect to explore online user-generated content (UGC) for intelligent customer hearing and product redesign strategy. UGC has the advantages of being more unbiased than traditional interviews, yielding in-time response, and widely accessible with a sheer volume. From online resources, customers’ preferences toward various aspects of the product can be exploited by promising sentiment analysis methods. However, due to the complexity of language, state-of-the-art sentiment analysis methods are still not accurate for practice use in product redesign. To tackle this problem, we propose an integrated customer hearing and product redesign system, which combines the robust use of sentiment analysis for customer hearing and coordinated redesign mechanisms. Ontology and expert knowledges are involved to promote the accuracy. Specifically, a fuzzy product ontology that contains domain knowledges is first learned in a semi-supervised way. Then, UGC is exploited with a novel ontology-based fine-grained sentiment analysis approach. Extracted customer preference statistics are transformed into multilevels, for the automatic establishment of opportunity landscapes and house of quality table. Besides, customer preference statistics are interactively visualized, through which representative customer feedbacks are concurrently generated. Through a case study of smartphone, the effectiveness of the proposed system is validated, and applicable redesign strategies for a case product are provided. With this system, information including customer preferences, user experiences, using habits and conditions can be exploited together for reliable product redesign strategy elicitation.

scholarly journals Challenges for Inclusive Finance Expansion: the Case of CrediAmigo, a Brazilian MFI

2011 ◽  
Vol 15 (3) ◽  
pp. 57-69 ◽  
Author(s):  
Fréderic Lavoie ◽  
Marlei Pozzebon1 ◽  
Lauro Gonzalez
Keyword(s):  
Social Inclusion ◽  
Qualitative Case Study ◽  
Financial Inclusion ◽  
Policy Makers ◽  
Critical Issues ◽  
Microfinance Institution

Investigating the replication of microcredit methodologies in Brazil, this paper seeks to explore the conditions under which microfinance and microcredit, two “financial inclusion” strategies, are likely to enhance social inclusion. A qualitative case study was conducted focusing the path of CrediAmigo, a Brazilian microfinance institution (MFI). This study is probably the first to provide an integrated perspective on critical issues concerning the replication of microcredit methodologies in Brazil. The findings may support MFI managers and policy makers in taking steps to facilitate the expansion of microfinance across Brazil.

Qualitative Case Study Research Design

2015 ◽  
Vol 25 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Jennifer Tetnowski
Keyword(s):  
Research Design ◽  
Real World ◽  
Qualitative Case Study ◽  
Case Study Research ◽  
Study Research

Qualitative case study research can be a valuable tool for answering complex, real-world questions. This method is often misunderstood or neglected due to a lack of understanding by researchers and reviewers. This tutorial defines the characteristics of qualitative case study research and its application to a broader understanding of stuttering that cannot be defined through other methodologies. This article will describe ways that data can be collected and analyzed.

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