Text-based emotion prediction system using machine learning approach

Nasir, Ahmad Fakhri Ab.; Nee, Eng Seok; Choong, Chun Sern; Ghani, Ahmad Shahrizan Abdul; Majeed, Anwar P. P. Abdul; Adam, Asrul; Furqan, Mhd.

doi:10.1088/1757-899x/769/1/012022

Cited by 30 publications

(10 citation statements)

References 16 publications

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“…Hence, there is a need to evaluate people's opinions or sentiments regarding text in cyberspace to avoid excessively negative emotions that can cause chaos. Research have shown that individual needs, opinions, emotions and behaviours can be revealed from analysing textual data [26]. Thus, a mechanism to monitor public opinion and emotions related to military security, political security, human security, homeland security, cybersecurity, economic security, environmental security as well as energy and natural resources security is crucial to maintain the stability of the country.…”

Section: (Balzacq 2015)mentioning

confidence: 99%

Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique

et al. 2023

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The internet offers a powerful medium for expressing opinions, emotions and ideas, using online platforms supported by smartphone usage and high internet penetration. Most internet posts are textual based and can include people's emotional feelings for a particular moment or sentiment. Monitoring online sentiments or opinions is important for detecting any excessive emotions triggered by citizens which can lead to unintended consequences and threats to national security. Riots and civil war, for instance, must be addressed due to the risk of jeopardizing social stability and political security, which are crucial elements of national security. Mining opinions according to the national security domain is a relevant research topic that must be enhanced. Mechanisms and techniques that can mine opinions in the aspect of political security require significant improvements to obtain optimum results. Researchers have noted that there is a strong relationship between emotion, sentiment and political security threats. This study proposes a new theoretical framework for predicting political security threats using a hybrid technique: the combination of lexicon-based approach and machine learning in cyberspace. In the proposed framework, Decision Tree, Naive Bayes, and Support Vector Machine have been deployed as threat classifiers. To validate our proposed framework, an experimental analysis is accomplished. The performance of each technique used in the experiments is reported. In this study, our proposed framework reveals that the hybrid Lexicon-based approach with the Decision Tree classifier recorded the highest performance score for predicting political security threats. These findings offer valuable insight to ongoing research on opinion mining in predicting threats based on the political security domain.

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Section: (Balzacq 2015)mentioning

confidence: 99%

Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique

et al. 2023

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“…So, 1 gram is just one word, it is also called a unigram, while 2 grams is called a bigram, 3 grams is called a trigram, and so on. When working with all n-grams with degrees less than or equal to n are generated [23].…”

Section: 𝐼𝐷𝐹 =mentioning

confidence: 99%

Comparative Analysis of KNN, Naïve Bayes and SVM Algorithms for Movie Genres Classification Based on Synopsis.

Buslim¹,

Hardy

et al. 2022

j. Teknik inform.

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Text classification is a process of categorizing a text into the correct label. Text classification in natural language processing is a challenging task that requires accuracy to get the correct results, manual text classification tends to be inefficient because it requires a lot of time and also experts. The utilization of machine learning for automatic text classification can be a solution to this problem. KNN, Naive Bayes, and SVM are known as some of the most algorithms to solve classification problems, especially text classification. In this study, we are trying to compare the KNN, Naive Bayes, and SVM algorithms for text classification with the problem of classifying movie genres based on a synopsis using datasets obtained from Kaggle.com and IMDB Dataset. The results of this study indicate that of the 12 experiments, Support Vector Machine (SVM) is the bestperforming algorithm with an accuracy of 90%, 93%, 65%, and 63%. It is hoped that this research can help to determine the best algorithm in the text classification process.

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“…The reason is that most information in the online system is displayed in textual form. A substantial amount of textual data can be generated since it is usual for an individual or persona in cyberspace to express emotions through words or text [ 150 ]. By utilising opinion mining in detecting threats in cyberspace, the state of national security can be strengthened.…”

Section: Future Research Directions Of Opinion Mining For National Securitymentioning

confidence: 99%

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

et al. 2021

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Background Opinion mining, or sentiment analysis, is a field in Natural Language Processing (NLP). It extracts people’s thoughts, including assessments, attitudes, and emotions toward individuals, topics, and events. The task is technically challenging but incredibly useful. With the explosive growth of the digital platform in cyberspace, such as blogs and social networks, individuals and organisations are increasingly utilising public opinion for their decision-making. In recent years, significant research concerning mining people’s sentiments based on text in cyberspace using opinion mining has been explored. Researchers have applied numerous opinions mining techniques, including machine learning and lexicon-based approach to analyse and classify people’s sentiments based on a text and discuss the existing gap. Thus, it creates a research opportunity for other researchers to investigate and propose improved methods and new domain applications to fill the gap. Methods In this paper, a structured literature review has been done by considering 122 articles to examine all relevant research accomplished in the field of opinion mining application and the suggested Kansei approach to solve the challenges that occur in mining sentiments based on text in cyberspace. Five different platforms database were systematically searched between 2015 and 2021: ACM (Association for Computing Machinery), IEEE (Advancing Technology for Humanity), SCIENCE DIRECT, SpringerLink, and SCOPUS. Results This study analyses various techniques of opinion mining as well as the Kansei approach that will help to enhance techniques in mining people’s sentiment and emotion in cyberspace. Most of the study addressed methods including machine learning, lexicon-based approach, hybrid approach, and Kansei approach in mining the sentiment and emotion based on text. The possible societal impacts of the current opinion mining technique, including machine learning and the Kansei approach, along with major trends and challenges, are highlighted. Conclusion Various applications of opinion mining techniques in mining people’s sentiment and emotion according to the objective of the research, used method, dataset, summarized in this study. This study serves as a theoretical analysis of the opinion mining method complemented by the Kansei approach in classifying people’s sentiments based on text in cyberspace. Kansei approach can measure people’s impressions using artefacts based on senses including sight, feeling and cognition reported precise results for the assessment of human emotion. Therefore, this research suggests that the Kansei approach should be a complementary factor including in the development of a dictionary focusing on emotion in the national security domain. Also, this theoretical analysis will act as a reference to researchers regarding the Kansei approach as one of the techniques to improve hybrid approaches in opinion mining.

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Text-based emotion prediction system using machine learning approach

Cited by 30 publications

References 16 publications

Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique

Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique

Comparative Analysis of KNN, Naïve Bayes and SVM Algorithms for Movie Genres Classification Based on Synopsis.

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

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