• Journal of KIISE
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• v.44 no.12
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• pp.1290-1295
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• 2017

Document classification, a task of classifying the category of each document based on text, is one of the fundamental areas for natural language processing. Document classification may be used in various fields such as topic classification and sentiment classification. Neural network models for document classification can be divided into two categories: word-level models and character-level models that treat words and characters as basic units respectively. In this study, we propose a neural network model that combines character-level and word-level models to improve performance of document classification. The proposed model extracts the feature vector of each word by combining information obtained from a word embedding matrix and information encoded by a character-level neural network. Based on feature vectors of words, the model classifies documents with a hierarchical structure wherein recurrent neural networks with attention mechanisms are used for both the word and the sentence levels. Experiments on real life datasets demonstrate effectiveness of our proposed model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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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.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.91-111
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• 2016

It is crucial for the social media based marketing practices to perform sentiment analyze the unstructured data written by the potential consumers of their products and services. In particular, when it comes to the companies which are interested in global business, the companies must collect and analyze the data from the social media of multinational settings (e.g. Youtube, Instagram, etc.). In this case, since the texts are multilingual, they usually translate the sentences into a certain target language before conducting sentiment analysis. However, due to the lack of cultural differences and highly qualified data dictionary, translated sentences suffer from misunderstanding the true meaning. These result in decreasing the quality of sentiment analysis. Hence, this study aims to propose a method to perform a multilingual sentiment analysis, focusing on Korean-Chinese cases, while avoiding language translations. To show the feasibility of the idea proposed in this paper, we compare the performance of the proposed method with those of the legacy methods which adopt language translators. The results suggest that our method outperforms in terms of RMSE, and can be applied by the global business institutions.

• Smart Media Journal
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• v.9 no.4
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• pp.144-151
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• 2020

In a mobile environment, communication takes place via SMS text messages. Vocabularies used in SMS texts can be expected to use vocabularies of different classes from those used in general Korean literary style sentence. For example, in the case of a typical literary style, the sentence is correctly initiated or terminated and the sentence is well constructed, while SMS text corpus often replaces the component with an omission and a brief representation. To analyze these vocabulary usage characteristics, the existing colloquial style corpus and the literary style corpus are used. The experiment compares and analyzes the vocabulary use characteristics of the colloquial corpus SMS text corpus and the Naver Sentiment Movie Corpus, and the written Korean written corpus. For the comparison and analysis of vocabulary for each corpus, the part of speech tag adjective (VA) was used as a standard, and a distinctive collexeme analysis method was used to measure collostructural strength. As a result, it was confirmed that adjectives related to emotional expression such as'good-','sorry-', and'joy-' were preferred in the SMS text corpus, while adjectives related to evaluation expressions were preferred in the Naver Sentiment Movie Corpus. The word embedding was used to automatically construct a sentiment lexicon based on the extracted adjectives with high collostructural strength, and a total of 343,603 sentiment representations were automatically built.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.285-307
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• 2019

In 2011, the Korean Medical Association revised the name of schizophrenia to remove the social stigma for the sick. Although it has been about nine years since the revision of the terminology, no studies have quantitatively analyzed how much social awareness has changed. Thus, this study investigates the changes in social awareness of schizophrenia caused by the revision of the disease name by analyzing Naver news articles related to the disease. For text analysis, LDA topic modeling, TF-IDF, word co-occurrence, and sentiment analysis techniques were used. The results showed that social awareness of the disease was more negative after the revision of the terminology. In addition, social awareness of the former term among two terms used after the revision was more negative. In other words, the revision of the disease did not resolve the stigma.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.189-194
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• 2016

본 연구는 다국어 감성사전 및 감성주석 코퍼스 구축 프로젝트인 MUSE 프로젝트의 일환으로 한국어 감성사전을 구축하기 위해 대표적인 영어 감성사전인 SentiWordNet을 이용하여 한국어 감성사전을 구축하는 방법의 의의와 한계점을 검토하는 것을 목적으로 한다. 우선 영어 SentiWordNet의 117,659개의 어휘중에서 긍정/부정 0.5 스코어 이상의 어휘를 추출하여 구글 번역기를 이용해 자동 번역하는 작업을 실시하였다. 그 중에서 번역이 되지 않거나, 중복되는 경우를 제거하고, 언어학 전문가들의 수작업으로 분류해낸 결과 3,665개의 감성어휘를 획득할 수 있었다. 그러나 이마저도 병명이나 순수 감성어휘로 보기 어려운 사례들이 상당수 포함되어 있어 실제 이를 코퍼스에 적용하여 감성어휘를 자동 판별했을 때에 맛집 코퍼스에서의 재현율(recall)이 긍정과 부정에서 각각 47.4%, 37.7%, IT 코퍼스에서 각각 55.2%, 32.4%에 불과하였다. 이와 더불어 F-measure의 경우, 맛집 코퍼스에서는 긍정과 부정의 값이 각각 62.3%, 38.5%였고, IT 코퍼스에서는 각각 65.5%, 44.6%의 낮은 수치를 보여주고 있어, SentiWordNet 기반의 감성사전은 감성사전으로서의 역할을 수행하기에 충분하지 않은 것으로 나타났다. 이를 통해 한국어 감성사전을 구축할 때에는 한국어의 언어적 속성을 고려한 체계적인 접근이 필요함을 역설하고, 현재 한국어 전자사전 DECO에 기반을 두어 보완 확장중인 SELEX 감성사전에 대해 소개한다.

• Annual Conference on Human and Language Technology
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• 2016.10a
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• pp.189-194
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• 2016

본 연구는 다국어 감성사전 및 감성주석 코퍼스 구축 프로젝트인 MUSE 프로젝트의 일환으로 한국어 감성사전을 구축하기 위해 대표적인 영어 감성사전인 SentiWordNet을 이용하여 한국어 감성사전을 구축하는 방법의 의의와 한계점을 검토하는 것을 목적으로 한다. 우선 영어 SentiWordNet의 117,659개의 어휘중에서 긍정/부정 0.5 스코어 이상의 어휘를 추출하여 구글 번역기를 이용해 자동 번역하는 작업을 실시하였다. 그 중에서 번역이 되지 않거나, 중복되는 경우를 제거하고, 언어학 전문가들의 수작업으로 분류해 낸 결과 3,665개의 감성어휘를 획득할 수 있었다. 그러나 이마저도 병명이나 순수 감성어휘로 보기 어려운 사례들이 상당수 포함되어 있어 실제 이를 코퍼스에 적용하여 감성어휘를 자동 판별했을 때에 맛집 코퍼스에서의 재현율(recall)이 긍정과 부정에서 각각 47.4%, 37.7%, IT 코퍼스에서 각각 55.2%, 32.4%에 불과하였다. 이와 더불어 F-measure의 경우, 맛집 코퍼스에서는 긍정과 부정의 값이 각각 62.3%, 38.5%였고, IT 코퍼스에서는 각각 65.5%, 44.6%의 낮은 수치를 보여주고 있어, SentiWordNet 기반의 감성사전은 감성사전으로서의 역할을 수행하기에 충분하지 않은 것으로 나타났다. 이를 통해 한국어 감성사전을 구축할 때에는 한국어의 언어적 속성을 고려한 체계적인 접근이 필요함을 역설하고, 현재 한국어 전자사전 DECO에 기반을 두어 보완 확장중인 SELEX 감성사전에 대해 소개한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.355-360
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• 2021

Recently Youtube has been more popular. As many studies show the comparative deprivation of the Social Medeia, this study looks into whether the comparative deprivation is expressed on the YouTube comments. It focuses on the Luxury Haul contents, videos about huge amounts of luxurious products, of which Youtubers'economic feature are demonstrative. The comments of the videos are analyzed with LDA TF-IDF and Word2Vec. Additionally, the comments were classified into positive and negative groups by the LSTM model as well. As a result of the study, even though many comments turned out positive, the negative keywords were indicated related to comparative deprivation. Also it was found that the viewers compared themselves with Youtubers. In particular, some YouTubers are more criticized if they are younger or does not seem to afford the luxurious products themselves. This study suggests that the users express the comparative deprivation on YouTube as well like on the other Social Media.

• Annual Conference on Human and Language Technology
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• 2013.10a
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• pp.53-58
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• 2013

온라인 소비자들은 amazon.com과 같은 온라인 상점 플랫폼에 상품 평가(리뷰: review) 글을 남김으로써 대상 상품에 대한 의견을 표현한다. 이러한 상품 리뷰는 다른 소비자들의 구매 결정에도 큰 영향을 끼친다는 관점에서 볼 때, 매우 중요한 정보원이라고 할 수 있다. 사람들이 남긴 의견 정보(opinion)를 자동으로 추출하거나 분석하고자 하는 연구인 감성 분석(sentiment analysis)분야에서 과거에 진행된 대다수의 연구들은 크게는 문서 단위에서 작게는 상품의 요소(aspect) 단위로 사용자들이 남긴 의견이 긍정적 혹은 부정적 감정을 포함하고 있는지 분석하고자 하였다. 이렇게 소비자들이 남긴 의견이 대상 상품 혹은 상품의 요소를 긍정적 혹은 부정적으로 판단했는지 여부를 판단하는 것이 유용한 경우도 있겠으나, 본 연구에서는 소비자들이 '어떤 관점'에서 대상 상품 혹은 상품의 요소를 평가했는지를 자동으로 추출하는 방법에 초점을 두었다. 본 연구에서는 형용사의 대표적인 성질 중 하나가 자신이 수식하는 명사의 속성에 값을 부여하는 것임에 주목하여, 수식된 명사의 속성을 추출하고자 하였고 이를 위해 WordNet을 사용하였다. 제안하는 방법의 효과를 검증하기 위해 3명의 평가자를 활용하여 실험을 하였으며 그 결과는 본 연구 방향이 감성분석에 있어 새로운 가능성을 열기에 충분하다는 것을 보여주었다.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.838-855
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• 2019

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.