• Journal of Information Processing Systems
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• v.19 no.3
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• pp.310-322
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• 2023

Although the network environment is gradually improving, the virtual nature of the network is still the same fact, which has brought a great influence on the supervision of Weibo network public opinion dissemination. In order to reduce this influence, the user information of Weibo network public opinion dissemination is studied by using Python technology. Specifically, the 2019 "Ethiopian air crash" event was taken as the research subject, the relevant data were collected by using Python technology, and the data from March 10, 2019 to June 20, 2019 were constructed by using the implicit Dirichlet distribution topic model and the naive Bayes classifier. The Weibo network public opinion user identity graph model under the "Ethiopian air crash" on June 20 found that the public opinion users of ordinary netizens accounted for the highest proportion and were easily influenced by media public opinion users. This influence is not limited to ordinary netizens. Public opinion users have an influence on other types of public opinion users. That is to say, in the network public opinion space of the "Ethiopian air crash," media public opinion users play an important role in the dissemination of network public opinion information. This research can lay a foundation for the classification and identification of user identity information types under different public opinion life cycles. Future research can start from the supervision of public opinion and the type of user identity to improve the scientific management and control of user information dissemination through Weibo network public opinion.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.1
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• pp.39-48
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• 2017

In a digital economy, users actively express their needs in many ways. Thus, many researchers analyze what users need and whether they are satisfied or not through opinion mining. In addition, they begin to find technology opportunities in heterogeneous technology fields. But they did not connect users' opinion to technology development process, only focused on natural language processing or marketing or manufacturing area. Also, heterogeneous technology fields are focused on fusion technology. Thus, this study suggests a novel approach that is based on sentimental value and can be applied to exploring technology opportunities in heterogeneous fields. Sentimental value is calculated from users' opinion through sLDA. The heterogeneous technology opportunity is explored by patent analysis. This research contributes to suggesting a hybrid methodology through patent and users' opinion. In addition, it can provide managerial efficiency by suggesting base data onto decision making.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.68-79
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• 2011

This paper focuses on the opinion information extraction and analysis system through Web mining that is based on statistics collected from Web contents. That is, users' opinion information which is scattered across several websites can be automatically analyzed and extracted. The system provides the opinion information search service that enables users to search for real-time positive and negative opinions and check their statistics. Also, users can do real-time search and monitoring about other opinion information by putting keywords in the system. Proposing technique proved that the actual performance is excellent by comparison experiment with other techniques. Performance evaluation of function extracting positive/negative opinion information, the performance evaluation applying dynamic window technique and tokenizer technique for multilingual information retrieval, and the performance evaluation of technique extracting exact multilingual phonetic translation are carried out. The experiment with typical movie review sentence and Wikipedia experiment data as object as that applying example is carried out and the result is analyzed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.3070-3075
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• 2010

Through Web 2.0 days, the end users express their opinions and thoughts for blogs and community spaces on the Internet. These opinions and thoughts are used to purchase products, however, users only refer to a few comments not overall opinions. Opinion Analysis System is an opinion search, developed from a natural language search, which analyzes the product's positive or negative evaluations using opinions of products and services on the Internet. In this paper, we suggest a syntactic analysis and inverse processing system that studies and processes 'Positive', 'Negative', 'Neutral' in addition to 'Inverse' information to analyze 'positive' or 'negative' for the core of sentences in Opinion Analysis Service.

• Journal of Information Technology Services
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• v.13 no.4
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• pp.397-410
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• 2014

For mobile service organizations, it is one of the most important tasks to reflect users' opinions rapidly and accurately. In this study, the process is defined to elicit requirements of software/system improvement for mobile application by extracting and refining from users' opinion in mobile app, and detailed activities procession method in this processing are also proposed. The process consists of 3 activities to get requirements of software/system improvement for mobile app. First activity is to transform mobile app to software structure and define term dictionary. Second activity is to elicit simple sentences based on software from users' opinion and refine them. The last activity is to integrate and adjust refined requirements. To verify the usability and validity of the proposed process and the methods, the outputs of manual processing and semi-automated processing were compared. As a result, efficiency and improvement possibility of the process were confirmed through extraction ratio of requirements, comparison of execution time, and analysis of agreement ratio.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2043-2051
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• 2017

Lately, In addition to the increased interest in the big data field, there is also a growing interest in application fields through the processing of big data. Opinion Mining is a big data processing technique that is widely used in providing personalized service to users. Based on this, in this paper, textual review of users' places is processed by Opinion mining technique and the sentiment of users was analyzed through k-means clustering. The same numerical value is given to users who have a similar category of sentiment classified as a clustering operation. We propose a method to show recommendation contents to users by predicting preference using collaborative filtering recommendation system with assigned numerical values and marking contents with markers on the map in order of places with high predicted value.

• Asian Journal for Public Opinion Research
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• v.8 no.3
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• pp.393-406
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• 2020

The discourse on the lockdown in Indonesia is getting stronger due to the increasing number of positive cases of the coronavirus and the death rate. As of August 12, 2020, the confirmed number of COVID-19 cases in Indonesia reached 130,718. There were 85,798 victims who have recovered and 5,903 who have died. Data show a significant increase in cases of COVID-19 every day. For this reason, there needs to be an evaluation of the government policy of the Republic of Indonesia in dealing with the COVID-19 pandemic in Indonesia. An evaluation of policies for handling the pandemic must include public opinion to determine any weaknesses of this policy. The development of public opinion about the lockdown policy can be understood through social media. During the COVID-19 pandemic, measuring public opinion through traditional methods (surveys) was difficult. For this reason, we utilized big data on social media as research data. The main purpose of this study is to understand public opinion on the lockdown policy in overcoming the COVID-19 pandemic in Indonesia. The things observed included: volume of Twitter users, top influencers, top tweets, and communication networks between Twitter users. For the methodological development of future public opinion research, the researchers outline the obstacles faced in researching public opinion based on big data from Twitter. The research results show that the lockdown policy is an interesting issue, as evidenced by the number of active users (79,502) forming 133,209 networks. Posts about the lockdown on Twitter continued to increase after the implementation of the lockdown policy on April 10, 2020. The lockdown policy has caused various reactions, seen from the word analysis showing 14.8% positive sentiment, 17.5% negative, and 67.67% non-categorized words. Sources of information who have played the roles of top influencers regarding the lockdown policy include: Jokowi (the president of the Republic of Indonesia), online media, television media, government departments, and governors. Based on the analysis of the network structure, it shows that Jokowi has a central role in controlling the lockdown policy. Several challenges were found in this study: 1) choosing keywords for downloading data, 2) categorizing words containing public opinion sentiment, and 3) determining the sample size.

• The Journal of the Korea Contents Association
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• v.16 no.4
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• pp.455-465
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• 2016

The purpose of this study was to examine the effect of twitter influenctials on users' willingness on express opinions. Based on the spiral of silence and corrective action hypothesis, the contradictory hypotheses were drawn. The online expeiment was conducted to verify which hypothesis is valid. Participant were assigned to the four experimenatal conditons: a famous influential's twit which agrees with the issue; a famous influential's twit which disagrees with the issue; an ordinary influential's twit to agree with the issue; an ordinary influential's twit to disagree with the issue. Results showed that opinion congruency with a twitter influential did not influence participants' willingness to express opinion online and offline, but the interaction effect with the type of influentials was found. Opinion dissonance with an ordinary tiwtterian increased willingness to express opinion. The findings suggest that twitter influentials, particulary an ordinary influentials with differnet opinions, can motivate users to express their own opinion.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.9
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• pp.59-70
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• 2016

The rapid development of the Chinese version of Twitter, the groom Weibo has become an important communication means for Chinese SNS users to obtain and share information. As a result, in China, the phenomenon of the power shift has emerged from the traditional opinion leaders to SNS opinion leasers. The relationship analysis of demographic variables of the Chinese SNS users and their Information on the relationship between keywords was made by utilizing the centrality analysis using Social Network Program NetMiner. China's SNS opinion leaders have general interest in daily activities with their families or friends rather than in social issues. And in case of SNS opinion leaders of high betweenness centrality, it was analyzed that general users was a key mediator role that organically out lead to the adjacent information. These properties are not independent of demographic variables, such as professional, therefore, the demographic characteristics of SNS opinion leaders showed a significant effect on the parameters of betweenness centrality. This study analyzed the characteristics of SNS users, especially opinion leaders in China by looking at the aspects of Chinese social phenomenon in terms of information. Through this study, we expect to provide basic information about the social characteristics of China through collective communication.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.111-119
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• 2016

In recent years, the opinion analysis is one of the key research fronts of any domain. Opinion target extraction is an essential process of opinion analysis. Target is usually referred to noun or noun phrase in an entity which is deliberated by the opinion holder. Extraction of opinion target facilitates the opinion analysis more precisely and in addition helps to identify the opinion polarity i.e. users can perceive opinion in detail of a target including all its features. One of the most commonly employed algorithms is a sequence labeling algorithm also called Conditional Random Fields. In present article, recent opinion target extraction approaches are reviewed based on sequence labeling algorithm and it features combinations by analyzing and comparing these approaches. The good selection of features combinations will in some way give a good or better accuracy result. Features combinations are an essential process that can be used to identify and remove unneeded, irrelevant and redundant attributes from data that do not contribute to the accuracy of a predictive model or may in fact decrease the accuracy of the model. Hence, in general this review eventually leads to the contribution for the opinion analysis approach and assist researcher for the opinion target extraction in particular.