• 정보화정책
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• 제17권3호
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• pp.3-20
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• 2010

본 연구는 소셜 컴퓨팅에 대한 기존의 연구동향을 분석하고, 향후 연구방향을 탐색하는데 목적이 있다. 이를 위해 2006년부터 최근까지 사회분야 학술지에 게재된 51편의 논문을 대상으로 연구주제 및 세부주제, 연구방법, 분석단위, 응용분야 측면에서 분석하였다. 분석결과, 소셜 컴퓨팅 분야에 대한 국내 연구의 몇 가지 경향이 발견되었다. 연구주제 측면에서는 조직 개념을 다룬 연구보다 사회 개념을 다룬 연구가 많았다. 조직 개념 분야의 연구에서는 기술 이전에 대한 연구가 많았으며, 사회 개념 분야의 연구에서는 문화적 이슈에 대한 연구가 많이 수행되었다. 연구방법으로는 현장 연구를, 분석단위로는 개인을 분석단위로 하는 연구가 지배적이었다. 응용분야 측면에서는 블로그에 대한 연구가 주를 이루고 있는 것으로 나타났다. 향후 소셜 컴퓨팅 연구의 발전을 위해서는 연구의 다양화가 요구된다.

• Journal of Information Processing Systems
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• 제16권3호
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• pp.718-732
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• 2020

Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2571-2586
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• 2022

The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3187-3200
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• 2020

Finding key users in microblog has been a research hotspot in recent years. There are two kinds of key users: obvious and hidden ones. Influence of the former is direct while that of the latter is indirect. Most of existing methods evaluate user's direct influence, so key users they can find usually obvious ones, and their ability to identify hidden key users is very low as hidden ones exert influence in a very covert way. Consequently, the algorithm of finding hidden key users based on topic transfer entropy, called TTE, is proposed. TTE algorithm believes that hidden key users are those normal users possessing a high covert influence on obvious ones. Firstly, obvious key users are discovered based on microblog propagation scale. Then, based on microblogs' topic similarity and time correlation, the transfer entropy from ordinary users' blogs to obvious key users is calculated and used to measure the covert influence. Finally, hidden influence degrees of ordinary users are comprehensively evaluated by combining above indicators with the influence of both ordinary users and obvious ones. We conducted experiments on Sina Weibo, and the results showed that TTE algorithm had a good ability to identify hidden key users.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.113-123
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• 2024

Stock market news sentiment analysis (SA) aims to identify the attitudes of the news of the stock on the official platforms toward companies' stocks. It supports making the right decision in investing or analysts' evaluation. However, the research on Arabic SA is limited compared to that on English SA due to the complexity and limited corpora of the Arabic language. This paper develops a model of sentiment classification to predict the polarity of Arabic stock news in microblogs. Also, it aims to extract the reasons which lead to polarity categorization as the main economic causes or aspects based on semantic unity. Therefore, this paper presents an Arabic SA approach based on the logistic regression model and the Bidirectional Encoder Representations from Transformers (BERT) model. The proposed model is used to classify articles as positive, negative, or neutral. It was trained on the basis of data collected from an official Saudi stock market article platform that was later preprocessed and labeled. Moreover, the economic reasons for the articles based on semantic unit, divided into seven economic aspects to highlight the polarity of the articles, were investigated. The supervised BERT model obtained 88% article classification accuracy based on SA, and the unsupervised mean Word2Vec encoder obtained 80% economic-aspect clustering accuracy. Predicting polarity classification on the Arabic stock market news and their economic reasons would provide valuable benefits to the stock SA field.