Sentiment Analysis of Madura Tourism in New Normal Era using Text Blob and KNN with Hyperparameter Tuning

Rachman, Fika Hastarita; Imamah, Imamah; Rintyarna, Bagus Setya

doi:10.1109/ismode53584.2022.9742894

Cited by 10 publications

(12 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…3, Health domain papers accounted for five papers of the final selection [10]- [14]. Then followed by four papers in political domains [13], [15]- [17], two papers about App [7], [8], and three papers in general domain [18]- [20], as well as one each on another domain: education [21], regulation [22], disaster [23], and tourism [24]. The general domain is public sentiment without a specific topic taken within a particular time.…”

Section: Resultsmentioning

confidence: 99%

“…4, the majority of the data for the sentiment analysis came from social media, especially Twitter. Twelve paper datasets were from Twitter [10], [11], [23], [24], [12]- [14], [16]- [19], [22], and one paper datasets each was from Instagram comments [15], student feedback [21], research answers [20], google play site [7] and play store [8].…”

Section: Rq2: What Data Sources Are Used In Sentiment Analysis Studie...mentioning

confidence: 99%

See 1 more Smart Citation

Lexicon-Based Sentiment Analysis Using Inset Dictionary: A Systematic Literature Review

Rufaida,

Permanasari,

Setiawan

2023

Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia

View full text Add to dashboard Cite

Sentiment analysis is one of the most exciting topics to research in text mining. Lexicon-based sentiment analysis produces good results across a wide range of conversational topics, is easily improved using various sources of information, and does not require additional training. However, only a few review papers have addressed sentiment analysis with specific lexicon dictionaries. As a result, neither data sources nor pre-processing techniques specific to a lexicon dictionary are discussed further. A Systematic Literature Review was conducted to provide a comprehensive and structured lexicon-based sentiment analysis using the Inset dictionary. The literature review resulted in selecting seventeen papers for a detailed study. The findings show that in the last five years, most sentiment analysis research using Inset focused on the health domain. Twitter provides the majority of the data for sentiment analysis. Stopword removal, tokenization, case folding, and stemming are common pre-processing techniques. This study also contained some additional observations from completed research.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Rq2: What Data Sources Are Used In Sentiment Analysis Studie...mentioning

confidence: 99%

Lexicon-Based Sentiment Analysis Using Inset Dictionary: A Systematic Literature Review

Rufaida,

Permanasari,

Setiawan

2023

Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia

View full text Add to dashboard Cite

show abstract

“…The model developed with deep learning algorithms performed better than the model developed with the traditional machine learning algorithms when trained on Textblob annotated text dataset [22], [23]. But, with a proper parameter fine tuning, performance of the traditional machine learning algorithms could be improved [24], [25].…”

Section: A Sentiment Analysis With Textblobmentioning

confidence: 92%

Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts

Ogbuokiri

Ahmadi

Nia

et al. 2024

IEEE Trans. Comput. Soc. Syst.

View full text Add to dashboard Cite

Many social media users express concerns about vaccines and their side effects on Twitter. These concerns lead to a compromise of confidence which brings about vaccine hesitancy. In Africa, vaccine hesitancy is a major challenge faced by health policymakers in the fight against COVID-19. Given that most tweets are geotagged, clustering them according to their sentiments could help identify locations that may likely experience vaccine hesitancy for health policy and planning. In this study, we collected 70 000 geotagged vaccine-related tweets in nine African countries, from December 2020 to February 2022. The tweets were classified into three sentiment classespositive, negative, and neutral. The quality of the classification outputs was achieved using Naíve Bayes (NB), logistic regression (LR), support vector machines (SVMs), decision tree (DT), and K-nearest neighbor (KNN) machine learning classifiers. The LR achieved the highest accuracy of 71% with an average area under the curve of 85%. The point-based location technique was used to calculate the hotspots based on the locations of the classified tweets. Locations with green, red, and gray backgrounds on the map signify a hotspot for positive, negative, and neutral sentiments. The outcome of this research shows that discussions on social media can be analyzed to identify hotspots during a disease outbreak, which could inform health policy in planning and management of vaccine hesitancy in Africa.

show abstract

“…User posts were classified as positive (1), neutral (0), and negative (-1). Although the Vader sentiment analyzer is commonly used for social media texts with an informal tone, TextBlob was a better choice for this study because user posts had a professional tone [74], [75]. In addition, the spaCy package was implemented for NER tasks to identify pedagogical and educational technology entities.…”

Section: ) Sentiment Named Entities and Entity Relationshipsmentioning

confidence: 99%

Examining the Characteristics of Practical Knowledge From Four Public Facebook Communities of Practice in Instructional Design and Technology

Leung

2022

IEEE Access

View full text Add to dashboard Cite

Instructional design and technology (IDT) professionals participate in communities of practice (CoPs) on Facebook to seek pedagogical and educational technology advice for solving instructional design (ID) problems. Much of the IDT literature has focused on formal educational environments and not on nonformal settings outside the classroom and beyond formal education. Further analysis of tacit or practical knowledge exchanged among community members is required to understand the purpose, functions, and organizational knowledge capital in online CoPs. To fill this gap, this study uses natural language processing (NLP) to analyze the practical knowledge of 6,066 anonymized users' posts from four large public IDT CoPs on Facebook from September 2017 to September 2020 after cleaning the dataset. User posts were publicly available and required no password authentication for access, including Instructional Designer (4,717), Designers for Learning (228), Adobe Captivate Users (599), and Articulate Storyline (522). The proposed methodology aims to extract practical knowledge of individual online CoPs in three parts. First, the characteristics of written communication among members are extracted by calculating word and sentence lengths, word frequencies, and contiguous words. Second, the characteristics of members' exchange of practical knowledge are obtained through sentiment identification, entity recognition, and relationships between pedagogical and educational technology entities. Third, the functions of individual online CoPs are developed through topic modeling with latent Dirichlet allocation (LDA) and BERTopic. The findings suggest similarities and differences among IDT CoPs, different resource distribution conventions, and members exchanging pedagogical and educational technology advice. The study highlights the need for pedagogical foundations to support instructional and technical decisions, mechanisms for self-assessment of practical knowledge concerning IDT competencies, community protocols for addressing misconceptions about learning, onboarding materials for new members, and new topic structures to classify practical knowledge. NLP tasks are implemented using Python libraries to support the future development of awareness tools.

show abstract

Sentiment Analysis of Madura Tourism in New Normal Era using Text Blob and KNN with Hyperparameter Tuning

Cited by 10 publications

References 13 publications

Lexicon-Based Sentiment Analysis Using Inset Dictionary: A Systematic Literature Review

Lexicon-Based Sentiment Analysis Using Inset Dictionary: A Systematic Literature Review

Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts

Examining the Characteristics of Practical Knowledge From Four Public Facebook Communities of Practice in Instructional Design and Technology

Contact Info

Product

Resources

About