Sentiment Analysis or Opinion Mining: A Review

Saad, Saidah; Saberi, Bilal

doi:10.18517/ijaseit.7.5.2137

Cited by 65 publications

(26 citation statements)

References 25 publications

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“…Haider et al (2021) affirm that feelings are expressed by opinions and emotions that usually are collectively defined as sentiments. A similar vision is shared also by Liu (2012) and by Saad and Saberi (2017), which considers the two terms as belonging essentially to the same concept. Medhat et al (2014) also affirm that the term SA and OM are interchangeable but they recognize that for some researchers the terms have a slightly different meaning.…”

Section: Sentiment Opinion Emotion and Affectmentioning

confidence: 78%

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

D’Aniello

Rocca

2022

Artif Intell Rev

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The 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.

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Section: Sentiment Opinion Emotion and Affectmentioning

confidence: 78%

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

D’Aniello

Rocca

2022

Artif Intell Rev

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“…That includes a location-filling task with intention detection. For instance, the intention can be a greeting, it can be informative where the user gives extra text data [38]. The task of padding locations and intention detection is perceived as a sequence labeling problem.…”

Section: The Natural Language Comprehension Unit -Detecting Intentmentioning

confidence: 99%

Towards developing a pocket therapist: an intelligent adaptive psychological support chatbot against mental health disorders in a pandemic situation

Salhi

Guemmat

Qbadou

et al. 2021

IJEECS

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Nowadays with COVID-19 ongoing epidemic outbreak, containment for weeks was one of the most effective measures adopted to deal with the spread of the virus until a vaccine could be efficient. Over that period, increased anxiety, depression, suicide attempts and post-traumatic stress disorder are accumulated. Several studies referred to the need of using chatbots, which recognizes human emotions in such pandemic contexts. More recently, numerous research papers improved the ability of artificial intelligence methods to recognize human emotion. However, they are still limited. The aim of this paper is the development of a chatbot against the disturbing psychic consequences of the pandemic, taking human emotion recognition into account. The object is to help people; especially students; suffering from mental disorders, by progressively understanding the reasonsbehind them. This innovative chatbot was developed by using the natural language processing model of deep learning. An advanced model of deep learning has been elaborated the intention for people and that to help them to regulate their mood and to reduce distortion of negative thoughts, that why a collection of a new database was done. The sequence-to-sequence model encoder and decoder consist of Long short-term memory cells and it is defined with the bi-directional dynamic recurrent neural network packets.

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“…Words that have a sound they are believed to be exceptionally close to each other Idiom. This is the last point in every respect they are controlled by a weight criterion for the poles of each idea phrases are inverted by separating evaluation of perspective and expression [58].…”

Section: Aspect Levelmentioning

confidence: 99%

A Survey: Sentimental Analysis on Product Reviews Using (MLT) Machine Learning Techniques and Approaches

2021

IJATCSE

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Social media sites are used today for the development of different types and nature of customers those use such benefits which are often shared by people on social media symbolic or textual opinions, ideas, and feelings. This attitude and orientation draw attention to research and analyze sentiments through online data about customer interest. Therefore, the sentimental analysis idea is proposed. This is among the various uses of Natural Language Processing (NLP) and Machine Learning Analysis (MLA) is very common. The main task of sentimental analysis is the classification of sentiments automatically into three categories that are positive, negative and neutral. Many classification researches are conducted over the years to know the exact feelings and situations of sentimental emotions of people. Classification, fuzzy and clustering, is used. To know the sentiment analysis of the people’s accurate feeling and situation, many times over the years classification research was conducted in past. The accuracy of classification is finding more in Fuzzy based. Fuzzy based classification finds more accurate and for comparative study execution Classical Text Classifications Model is used. In comparative performance, this study shows the possibility of implementing the proposed method able to provide more accurate results when it comes in comparison with conventional classifiers. In this article we have discussed different researchers worked on the method of sentiment analysis and classification. This article also shows the importance of extracting comments and analyze sentiments

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Sentiment Analysis or Opinion Mining: A Review

Cited by 65 publications

References 25 publications

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

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

Towards developing a pocket therapist: an intelligent adaptive psychological support chatbot against mental health disorders in a pandemic situation

A Survey: Sentimental Analysis on Product Reviews Using (MLT) Machine Learning Techniques and Approaches

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