Sentiment Analysis of Twitter Data Using Clustering and Classification

Modak, Santanu; Mondal, Abhoy Chand

doi:10.1007/978-981-19-1142-2_51

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…Modak et al [37] extensively explore the correlation between sarcasm and sentiment analysis, utilizing Twitter data. They employ an innovative approach involving the fusion of K-means clustering, Principal Component Analysis (PCA), and Support Vector Machine (SVM) classifiers.…”

Section: Joint Sarcasm Detection and Sentiment Analysismentioning

confidence: 99%

A Survey of Sentiment Analysis and Sarcasm Detection

Derbala Yacoub,

O. Slim,

Elsayed Aboutabl

2024

Int. j. electr. comput. eng. syst. (Online)

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In recent years, more people have been using the internet and social media to express their opinions on various subjects, such as institutions, services, or specific ideas. This increase highlights the importance of developing automated tools for accurate sentiment analysis. Moreover, addressing sarcasm in text is crucial, as it can significantly impact the efficacy of sentiment analysis models. This paper aims to provide a comprehensive overview of the conducted research on sentiment analysis and sarcasm detection, focusing on the time from 2018 to 2023. It explores the challenges faced and the methods used to address them. It conducts a comparison of these methods. It also aims to identify emerging trends that will likely influence the future of sentiment analysis and sarcasm detection, ensuring their continued effectiveness. This paper enhances the existing knowledge by offering a comprehensive analysis of 40 research works, evaluating performance, addressing multilingual challenges, and highlighting future trends in sarcasm detection and sentiment analysis. It is a valuable resource for researchers and experts interested in the field, facilitating further advancements in sentiment analysis techniques and applications. It categorizes sentiment analysis methods into ML, lexical, and hybrid approaches, highlighting deep learning, especially Recurrent Neural Networks (RNNs), for effective textual classification with labeled or unlabeled data.

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Section: Joint Sarcasm Detection and Sentiment Analysismentioning

confidence: 99%

A Survey of Sentiment Analysis and Sarcasm Detection

Derbala Yacoub,

O. Slim,

Elsayed Aboutabl

2024

Int. j. electr. comput. eng. syst. (Online)

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“…[9] Studied the development and evaluation methods of ontologybased recommender systems and discussed technical ontology use and the recommendation process. [8] developed a model of sarcasm detection formed by fusing K-mean, PCA, and SVM classifiers. The ontology was generated to analyze the sentiments of Oman tourists.…”

Section: Related Workmentioning

confidence: 99%

Ontology Supported Case-Based Reasoning for Tourist Knowledge Discovery

Mishra,

Singh

2023

IRJIET

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Agra, city of the Taj Mahal, is a famous tourist spot that attracts people from all over the world. The issue that arises is how to store tourism information of Agra more semantically and make the information reusable and sharable so that it enables effective search. This research presents OCBR-Tour System that integrates ontology and case-based reasoning in the tourism domain. Ontology models are developed into ontology forms with class, each class is divided into subclasses in which the relationships of them are direct properties of the class object property, and data property. The research result provides the ontology on information of tourism components in Agra. The ontology is used for information retrieval using DL queries. Case-based reasoning is used to predict the attractions to visit in the city. Rules are generated to further enhance the tour plan for the tourists.

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