Exploring human emotion via Twitter

Riyadh, Abu Zonayed; Alvi, Nasif; Talukder, Kamrul Hasan

doi:10.1109/iccitechn.2017.8281813

Cited by 15 publications

(5 citation statements)

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“…More specifically, we describe how we collect and analyze necessary information from social media, perform data classification, and use the QA system answering process. The Twitter application programming interface (API) is used to access data in the JavaScript Object Notation (JSON) String form [28]. After that, all of the keywords are translated from Japanese into English in order to facilitate understanding while preparing for the next step, as shown in Figure 4.…”

Section: Methodsmentioning

confidence: 99%

Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster

Kemavuthanon

Uchida

2020

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Natural disasters are events that humans cannot control, and Japan has suffered from many such disasters over its long history. Many of these have caused severe damage to human lives and property. These days, numerous Japanese people have gained considerable experience preparing for disasters and are now striving to predict the effects of disasters using social network services (SNSs) to exchange information in real time. Currently, Twitter is the most popular and powerful SNS tool used for disaster response in Japan because it allows users to exchange and disseminate information quickly. However, since almost all of the Japanese-related content is also written in the Japanese language, which restricts most of its benefits to Japanese people, we feel that it is necessary to create a disaster response system that would help people who do not understand Japanese. Accordingly, this paper presents the framework of a question-answering (QA) system that was developed using a Twitter dataset containing more than nine million tweets compiled during the Osaka North Earthquake that occurred on 18 June 2018. We also studied the structure of the questions posed and developed methods for classifying them into particular categories in order to find answers from the dataset using an ontology, word similarity, keyword frequency, and natural language processing. The experimental results presented herein confirm the accuracy of the answer results generated from our proposed system.

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Section: Methodsmentioning

confidence: 99%

Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster

Kemavuthanon

Uchida

2020

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“…They have used different machine learning classifier algorithms for their proposed model where they have used 90 depressed and 90 non-depressed user's data. In [5], the authors have described a system which can analyze emotions on twitter data. The basic emotions of a human being like happiness, sadness, surprise, disgust etc.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Depression on Social Media Utilizing Machine Learning and Deep Learning Methods

Paul,

Ahmed,

Hasan

et al. 2023

INDJCSE

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Depression is a psychological phenomenon that affects the metal health of people. At the maximum level of depression, one may commit suicide. Besides, it hampers the family and social activities. As a result, identifying depressed persons is critical before proceeding with physiological treatment. Social media has become the most powerful platform for expressing feelings of individuals and also became a potential source of depressive data. Some handy research works are available for English data while it is poor for Bengali data. We have created a noble dataset in Bengali and applied different machine learning and deep learning methods for performance evaluation. A combined method of CNN and GRU gives us the highest accuracy of 87.11%. Our research finds that the performance of CNN+GRU is better compared to other previous research works.

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“…It was tokenizing [17] from the Japanese sentences of the Osaka North Earthquake on June 18, 2018, such as; noun, verb, adverb, adjective, emoji, hashtag, link, and @Addfriend [18]. These APIs can also be used to access Twitter data [19] and the data from API is JSON String file [20]. Then, all of the keywords have been translated into English to know the meaning and understanding of each keyword by google translate API [21].…”

Section: Dataset Gatheringmentioning

confidence: 99%

Classification of Social Media Messages Posted at the Time of Disaster

Kemavuthanon

Uchida

2020

IFIP Advances in Information and Communication Technology

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Nowadays, social media is one of the essential sharing of information and proliferation tools because it spreads text messages, news, pictures, or videos in real-time. During the disaster, Japanese people use social media to exchange real-time information for their social interaction. Twitter is the most popular tool that has been used for disaster response in Japan. Even though many disaster systems have been created and used for disaster mitigation in Japan, most of them are assumed to be used by the Japanese in the Japanese language. From this problem, this study focuses on the way to create a disaster response system and community service to help, collect, and extract information on social media to help disaster mitigation becomes more important. This paper aims to investigate the tweets by focusing on noun keywords during the Osaka North Earthquake on 18 June 2018 with a data set of more than 9,000,000 tweets. The process presented classify social media messages by using ontology, word similarity, frequency of keyword, and evaluate results of natural language processing. We organize the messages into 15 categories and used as the classification algorithms with machine learning features of the count of each category word in the sentences. The result tweets were statistically compared with the keyword in each category to classify the content and collecting disaster information and using the result to build the analysis system.

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Exploring human emotion via Twitter

Cited by 15 publications

References 1 publication

Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster

Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster

Analyzing Depression on Social Media Utilizing Machine Learning and Deep Learning Methods

Classification of Social Media Messages Posted at the Time of Disaster

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