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Journal articles on the topic 'Opinion mining(OM)'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Rani, Mikanshu, and Jaswinder Singh. "Optimization of opinion mining classification techniques using dragonfly algorithm." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3567. http://dx.doi.org/10.11591/ijai.v13.i3.pp3567-3575.

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<p>With the rapid evolution and growth of the internet, many individuals are using websites, blogs, and social media, and sharing their opinions about any product or service on online social platforms. Opinion mining (OM) is a field of analyzing opinions or reviews given by the public about services or products on online resources into positive, negative, or neutral classes. Governments, businesses, and researchers are using OM to analyze the reviews or opinions of the public. Thus, OM is helping individuals and businesses in better decision making. This paper mainly focuses on the featu

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Xia, Lixin, Zhongyi Wang, Chen Chen, and Shanshan Zhai. "Research on feature-based opinion mining using topic maps." Electronic Library 34, no. 3 (2016): 435–56. http://dx.doi.org/10.1108/el-11-2014-0197.

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Purpose Opinion mining (OM), also known as “sentiment classification”, which aims to discover common patterns of user opinions from their textual statements automatically or semi-automatically, is not only useful for customers, but also for manufacturers. However, because of the complexity of natural language, there are still some problems, such as domain dependence of sentiment words, extraction of implicit features and others. The purpose of this paper is to propose an OM method based on topic maps to solve these problems. Design/methodology/approach Domain-specific knowledge is key to solve

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3

Essameldin, Reem, Ahmed A. Ismail, and Saad M. Darwish. "Quantifying Opinion Strength: A Neutrosophic Inference System for Smart Sentiment Analysis of Social Media Network." Applied Sciences 12, no. 15 (2022): 7697. http://dx.doi.org/10.3390/app12157697.

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The contemporary speed at which opinions move on social media makes them an undeniable force in the field of opinion mining (OM). This may cause the OM challenge to become more social than technical. This is when the process can determinately represent everyone to the degree they are worth. Nevertheless, considering perspectivism can result in opinion dynamicity. Pondering the existence of opinion dynamicity and uncertainty can provide smart OM on social media. This study proposes a neutrosophic-based OM approach for Twitter that handles perspectivism, its consequences, and indeterminacy. For

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Vishwakarma, Shweta. "A Review Paper on Sentiment Analysis using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 528–31. http://dx.doi.org/10.22214/ijraset.2023.56545.

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Abstract: Opinion Mining (OM) or Sentiment Analysis (SA) can be described as the process of identifying, extracting, and categorizing viewpoints on various subjects. It falls under the domain of natural language processing (NLP) and is commonly employed to gauge public sentiment towards specific laws, policies, marketing campaigns, and more. This involves the development of methodologies to collect and analyze comments and opinions posted on social media platforms concerning legislation, regulations, and other related matters. Information extraction plays a pivotal role in this process, as it

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Adinarayana, Salina, and E. Ilavarasan. "A Machine Learning Approach to Extract Opinions from Social Media Content." International Journal of Engineering & Technology 7, no. 4.5 (2018): 257. http://dx.doi.org/10.14419/ijet.v7i4.5.20080.

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The Opinion Mining (OM) from mobile based social media content (SMC) is more challenging compared to topic-based mining, and it cannot be performed based on just examining the presence of single words in the text containing opinion expressions. Moreover, the existing systems of opinion classification find that a large number of features that are not feasible for the mobile environment. The existing methods of OM in this mobile environment do not consider the semantic orientation of the SMC in the review. The proposed machine learning approach extends the feature-based classification approach t

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Parth, Deshmukh, Gadge Adesh, Ganbote Aniket, Garud Swapnali, and D. S. Kulkarni Prof. "Survey on Sentiment Analysis Using Machine Learning." Journal of Control System and Control Instrumentation 5, no. 1 (2019): 19–24. https://doi.org/10.5281/zenodo.2614704.

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Sentiment Analysis is the mining of opinions, sentiments & subjectivity of the context. It is the process of computationally identifying & categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral. Sentiment Analysis (SA) or Opinion Mining (OM) is the study of people’s opinions, monitoring social media & other online resources for customer reviews to understand customer understanding of significant in business analysis. SA or OM is used over

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7

Rifat Mustafa, Shabana Rai, Ubaid Ullah, and Muhammad Sohaib Naz. "Summary in General Summary of an Overview of Opinion Mining." Journal of Advancement in Computing 1, no. 1 (2023): 9–13. http://dx.doi.org/10.36755/jac.v1i1.47.

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The internet is a very effective resource for solving all problems in the present era. The world's population as a whole spends one-third of their time and money using the internet. People learn things from it in every aspect of life, including education, entertainment, communication, shopping, etc. In order to achieve this, consumers take use of websites and share comments or opinions about various goods, services, events, etc. based on their personal experiences. In this way, the input from those webs is composed into a sizable amount of textual data that can be investigated, assessed, and c

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Mohamed, Mohamed Hegazy, Sayed Abdelgaber, and Laila Abd-Ellatif. "Enhancing the Performance of Educational Systems using Efficient Opinion Mining Techniques." Journal of Education and e-Learning Research 10, no. 1 (2022): 19–28. http://dx.doi.org/10.20448/jeelr.v10i1.4335.

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Governments and educational authorities around the world are emphasizing performance evaluation of educational systems. Opinion Mining (OM) has gained acceptance among experts in various regions, including the preparation space. The proposed model involves Two modules: the data preprocessing module and the opinion mining module. The main objective of our article is to enhance educational systems through the analysis of student comments, teacher comments and course comments. Furthermore, the proposed model uses a bundling task to make groups of packs for students from its comments. The datasets

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Bouras, Dalila, Mohamed Amroune, Hakim Bendjenna, and Nabiha Azizi. "Techniques and Trends for Fine-Grained Opinion Mining and Sentiment Analysis: Recent Survey." Recent Advances in Computer Science and Communications 13, no. 2 (2020): 215–27. http://dx.doi.org/10.2174/2213275912666181227144256.

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Background: Nowadays, with the appearance of web 2.0, more users express their opinions, judgments, and thoughts towards certain objects, services, organizations, and their attributes via social networking, forum entries, websites, and blogs and so on. In this way, the volume of raw content generated by these users will increase rapidly with enormous size, where people often find difficulties in identifying and summarizing fine-grained sentiments buried in the opinion-rich resources. The traditional opinion mining techniques, which focused on the overall sentiment of the review, fails to uncov

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10

Zaw, Myint, and Pichaya Tandayya. "Product Categorization for Social Marketing Applying the RFC Model and Data Science Techniques." International Journal of Business Analytics 7, no. 4 (2020): 43–62. http://dx.doi.org/10.4018/ijban.2020100104.

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Currently, it is the age of social market due to the growth of internet technologies. The marketers require the complete information of customer perspectives on products and services comparing with others. The RFM (recency, frequency, and monetary) model is a technique to measure a comparison of information, especially in traditional market analytics. Over the past decade, social market big data (SMBD), especially feedback, has been used to understand customer satisfaction. This paper proposes a new approach to classify the products from feedbacks, called the RFC (recency, frequency, and credi

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Prof., Richa Mehra, Saxena Diksha, and Gupta |. Joy Joseph Shubham. "Sentiment Analysis." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 1370–73. https://doi.org/10.31142/ijtsrd23375.

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Sentiment Analysis SA is an ongoing field of research in text mining field. SA sentiment analysis is the computational treatment of opinions, sentiments and text. This s paper deals in a comprehensive overview of the recent updates in this field. Many recently proposed algorithms amend and various SA applications are investigated and presented briefly in this paper. The related fields to SA transfer learning, emotion detection, and building resources that attracted researchers recently are discussed. The main objective of this paper is to give nearly full image of SA techniques and the related

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12

Nuha, Elamin, A.Talab Samani, and Khalid Ahmed. "Sentiment Analysis with Supervised Learning Techniques: A Survey." Indian Journal of Science and Technology 13, no. 3 (2020): 249–68. https://doi.org/10.17485/ijst/2020/v13i03/148900.

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Abstract <strong>Objectives:</strong> This study aims two main goals; one is to provide complete notions relevant to sentiment analysis by SA mechanisms, its categorization, and its techniques. The second goal is to make a comprehensive study of supervised learning techniques used in SA classification to summarize the different works conducted in this area and track the recent developments. <strong>Methods:</strong> To achieve the first goal, several important survey studies, including modern and relevant works presented would be analyzed for full concepts around SA. As for the secon

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Fajri, Faisal, Bambang Tutuko, and Sukemi Sukemi. "Membandingkan Nilai Akurasi BERT dan DistilBERT pada Dataset Twitter." JUSIFO (Jurnal Sistem Informasi) 8, no. 2 (2022): 71–80. http://dx.doi.org/10.19109/jusifo.v8i2.13885.

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The growth of digital media has been incredibly fast, which has made consuming information a challenging task. Social media processing aided by Machine Learning has been very helpful in the digital era. Sentiment analysis is a fundamental task in Natural Language Processing (NLP). Based on the increasing number of social media users, the amount of data stored in social media platforms is also growing rapidly. As a result, many researchers are conducting studies that utilize social media data. Opinion mining (OM) or Sentiment Analysis (SA) is one of the methods used to analyze information conta

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14

M., Azhagiri. "Empirical Study on Sentiment Analysis." Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) 3, no. 1 (2023): 8–18. https://doi.org/10.54105/ijainn.B1044.123122.

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<strong>Abstract: </strong>Sentiment analysis (SA), generally known as Opinion Mining (OM), is really the process of gathering and evaluating people's ideas, thoughts, feelings, beliefs, including views about various subjects, goods, as well as services. Individuals produce large amounts of comments and evaluations about products, services, and day-to-day tasks as Internet-based applications such as webpages, online sites, social networking sites, and blog posts continue to evolve at a rapid pace. Firms, government institutions medical researchers and scholars may use sentiment analysis to col

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15

Radha, Guha, and Sutikno Tole. "Natural language understanding challenges for sentiment analysis tasks and deep learning solutions." International Journal of Informatics and Communication Technology 11, no. 3 (2022): 247–56. https://doi.org/10.11591/ijict.v11i3.pp247-256.

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When it comes to purchasing a product or attending an event, most people want to know what others think about it first. To construct a recommendation system, a user's likeness of a product can be measured numerically, such as a five-star rating or a binary like or dislike rating. If you don't have a numerical rating system, the product review text can still be used to make recommendations. Natural language comprehension is a branch of computer science that aims to make machines capable of natural language understanding (NLU). Negative, neutral, or positive sentiment analysis (SA) or op

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16

Neelakandan, S., and D. Paulraj. "A gradient boosted decision tree-based sentiment classification of twitter data." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 04 (2020): 2050027. http://dx.doi.org/10.1142/s0219691320500277.

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People communicate their views, arguments and emotions about their everyday life on social media (SM) platforms (e.g. Twitter and Facebook). Twitter stands as an international micro-blogging service that features a brief message called tweets. Freestyle writing, incorrect grammar, typographical errors and abbreviations are some noises that occur in the text. Sentiment analysis (SA) centered on a tweet posted by the user, and also opinion mining (OM) of the customers review is another famous research topic. The texts are gathered from users’ tweets by means of OM and automatic-SA centered on te

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17

Guha, Radha, and Tole Sutikno. "Natural language understanding challenges for sentiment analysis tasks and deep learning solutions." International Journal of Informatics and Communication Technology (IJ-ICT) 11, no. 3 (2022): 247. http://dx.doi.org/10.11591/ijict.v11i3.pp247-256.

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<span>When it comes to purchasing a product or attending an event, most people want to know what others think about it first. To construct a recommendation system, a user's likeness of a product can be measured numerically, such as a five-star rating or a binary like or dislike rating. If you don't have a numerical rating system, the product review text can still be used to make recommendations. Natural language comprehension is a branch of computer science that aims to make machines capable of natural language understanding (NLU). Negative, neutral, or positive sentiment analysis (SA) o

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18

Azhagiri, M., S. Divya Meena, A. Rajesh, M. Mangaleeswaran, and M. Gowtham Sethupathi. "Empirical Study on Sentiment Analysis." Indian Journal of Artificial Intelligence and Neural Networking 3, no. 1 (2023): 8–18. http://dx.doi.org/10.54105/ijainn.b1044.123122.

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Abstract:

Sentiment analysis (SA), generally known as Opinion Mining (OM), is really the process of gathering and evaluating people's ideas, thoughts, feelings, beliefs, including views about various subjects, goods, as well as services. Individuals produce large amounts of comments and evaluations about products, services, and day-to-day tasks as Internet-based applications such as webpages, online sites, social networking sites, and blog posts continue to evolve at a rapid pace. Firms, government institutions medical researchers and scholars may use sentiment analysis to collect and evaluate mood of t

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19

"Feature Selection Optimization for Highlighting Opinions Using Supervised and Unsupervised Learning on Arabic Language." International Journal of Advanced Trends in Computer Science and Engineering 10, no. 2 (2021): 636–42. http://dx.doi.org/10.30534/ijatcse/2021/251022021.

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Text mining utilizes machine learning (ML) and natural language processing (NLP) for text implicit knowledge recognition, such knowledge serves many domains as translation, media searching, and business decision making. Opinion mining (OM) is one of the promised text mining fields, which are used for polarity discovering via text and has terminus benefits for business. ML techniques are divided into two approaches: supervised and unsupervised learning, since we herein testified an OM feature selection(FS)using four ML techniques. In this paper, we had implemented number of experiments via four

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20

Joylin Denita Dsouza, Shabarish S.K, Anushree Raj. "COMPARATIVE STUDY ON SENTIMENTAL ANALYSIS AND OPINION MINING THROUGH ONLINE CUSTOMER REVIEWS." Redshine Archive 2 (July 7, 2020). http://dx.doi.org/10.25215/8119070771.21.

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In daily life, consumer opinions are quite important. When we need to make a decision, we consider the opinions of other individuals. On blogs, review websites, and social networking sites nowadays, many internet users express their opinions on a wide range of items. The number of people using the internet to purchase goods is rising, and there are more and more results being stored online as a result. As a consequence, more users are writing reviews or comments every day. Organisations in the business and corporate world are always curious to hear what customers or other people have to say ab

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21

"Sentiment Analysis for Customer Opinion on Hotel using Machine Learning Techniques." International Journal of Innovative Technology and Exploring Engineering 8, no. 12 (2019): 5211–13. http://dx.doi.org/10.35940/ijitee.l2781.1081219.

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Opinions from others play a significant part to take our own decision, The people’s opinions, attitudes and emotions are a computational study toward an entity is called as Sentiment Analysis (SA) or Opinion Mining (OM). In today's world, everything like business, organization and even individuals wants to know opinion from public or customers about their presentation, products and about their services which will give clear idea about their product, portfolio in the market and if these services is not up to the mark how their services they improve, so that their business will perform better. T

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22

"Aspect Term Extraction for Aspect Based Opinion Mining." International Journal of Innovative Technology and Exploring Engineering 8, no. 11 (2019): 2228–33. http://dx.doi.org/10.35940/ijitee.k2050.0981119.

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Opinion Mining (OM) is also called as Sentiment Analysis (SA). Aspect Based Opinion Mining (ABOM) is also called as Aspect Based Sentiment Analysis (ABSA). In this paper, three new features are proposed to extract the aspect term for Aspect Based Sentiment Analysis (ABSA). The influence of the proposed features is evaluated on five classifiers namely Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Conditional Random Fields (CRF). The proposed features are evaluated on the Two datasets on Restaurant and Laptop domains available in International W

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