Opinion Analysis for Emotional Classification on Emoji Tweets using the Naïve Bayes Algorithm

Sendari, Siti; Zaeni, Ilham Ari Elbaith; Lestari, Dian Candra; Hariyadi, Hanny Prasetya

doi:10.17977/um018v3i12020p50-59

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…This is a common method of defining and grouping opinions about goods, services, or concepts, involving data mining applications, artificial intelligence (AI), and machine learning (ML) to mine text with a sentiment or subjective meaning. Opinion analysis is one type of study used in social media because its content can become a trending topic and significantly impact social life [12]. Text Mining is one of the main subfields of data mining.…”

Section: Methodsmentioning

confidence: 99%

Uncovering negative sentiments: a study of indonesian twitter users' health opinions on coffee consumption

Budiarto,

Rokhman,

Uriu

2023

Bull.Socinf.The.App

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The increase in coffee consumption among the public is due to several reasons, including health and lifestyle reasons. Awareness of the positive and negative effects of coffee consumption has also increased in society. This research is a sentiment analysis that aims to investigate Twitter users' opinions about the impact of coffee consumption on their health. The method used involves data collection using the RapidMiner application, utilizing the Twitter Application Programming Interface (API) function connected to a prepared Twitter account. The obtained data underwent data cleaning, saved as an Excel file type, training and testing, and model evaluation. Then, the data was classified into three categories: Negative Opinion, Neutral Opinion, and Positive Opinion. The results showed that less than 10% of opinions were positive, 19% were neutral, and 73% were negative. The opinions obtained are useful information for stakeholders in the coffee industry. They can also be used to determine better steps in educating the public about coffee.

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Section: Methodsmentioning

confidence: 99%

Uncovering negative sentiments: a study of indonesian twitter users' health opinions on coffee consumption

Budiarto,

Rokhman,

Uriu

2023

Bull.Socinf.The.App

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“…[176], [246], [159], [208], [283], [309] Long-Term Preservation If chatbots are used in cultural heritage preservation, there must be considerations for the long-term preservation of data and content.…”

Section: Inclusivity and Accessibilitymentioning

confidence: 99%

“…-[15],[100]-[122],[113],[123]-[141],[309]-[310] 2021 40 [5], [142]-[166], [8], [167]-[169], [16], [170]-[177], [307] 2022 69 [2]-[4],…”

mentioning

confidence: 99%

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Suryanto,

Wibawa,

Hariyono

et al. 2023

IJRCS

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Chatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field.

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“…The characteristic of Bayesian method is to combine a priori probability and a posteriori probability, that is, it avoids the subjective bias of using only a priori probability and the overfitting phenomenon of using sample information alone. The results show that the "happiness" category has become the emotional trend of Twitter users, in which emojis are dominant [2]. Due to the increase in the number of applications and their use in various fields, the research of text emotion classification has been growing steadily in the past few years.…”

Section: Related Workmentioning

confidence: 99%

Emotion Classification Method of Financial News Based on Artificial Intelligence

Zheng

2022

Wireless Communications and Mobile Computing

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With the continuous development of economy, the economic development model is constantly changing. Especially since China’s entry into WTO, the scale of economic development has reached a new height. The continuous development of economy makes the financial news module evolve towards specialization. However, with the emergence of Internet of Things technology, a large number of data appear in the network, which brings some difficulties to the classification and analysis of data economy. Emotion classification refers to the complexity and diversity of people’s emotions. It can be classified from different observation angles. Because the core content of emotion is value, human emotion should be classified mainly according to the different characteristics of the movement and change of value relationship it reflects. This paper is aimed at studying the emotional classification method of financial news based on artificial intelligence and expecting to use artificial intelligence technology and classification method to classify financial news. It allows more people to know the implied information of financial information and promotes economic development. Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. This paper mainly summarizes the topic selection characteristics and subdivision topic selection characteristics of financial data news through quantitative and qualitative methods and explores the classification of financial news. In this paper, a simplified classification algorithm based on convolution function is proposed for the classification of traditional financial news networks. The experimental results show that the classification accuracy of artificial intelligence method is improved by 4% compared with the traditional emotion classification method, and the classification accuracy of positive emotion is lower than that of negative emotion by 2%.

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Opinion Analysis for Emotional Classification on Emoji Tweets using the Naïve Bayes Algorithm

Cited by 12 publications

References 24 publications

Uncovering negative sentiments: a study of indonesian twitter users' health opinions on coffee consumption

Uncovering negative sentiments: a study of indonesian twitter users' health opinions on coffee consumption

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Emotion Classification Method of Financial News Based on Artificial Intelligence

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