• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.506-509
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• 2017

Opinion mining is a way to analyze the emotions in the text and is used to identify the emotional state of the author and to find out the opinions of the public. As you can analyze individual emotions through opinion mining, if you analyze the text by region, you can find out the emotional state you have in each region. The regional sentiment analysis can obtain information that could not be obtained from personal sentiment analysis, and if a certain area has emotions, it can understand the cause. For regional sentiment analysis, we need text data created by region, so we need to collect data through Twitter crawling. Therefore, this paper designs a Twitter data collection system for regional sentiment analysis. The client requests the tweet data of the specific region and time, and the server collects and transmits the requested tweet data from the client. Through the latitude and longitude values of the region, it collects the tweet data of the area, and it can manage the text by region and time through collected data. We expect efficient data collection and management for emotional analysis through the design of this system.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.517-528
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• 2023

The purpose of this study is to analyze and compare UX factors of connectivity systems of domestic and overseas car brands. Using a text mining analysis, UX factors of domestic and overseas brands were compared through positive-negative sentiment index. After collecting 120,000 reviews on Hyundai Motor Group (Hyundai, Kia, Genesis) and 190,000 on Tesla, BMW, and Mercedes, pre-processing was performed. Keywords were classified into 11 UX factors in 3 dimensions of the system connection, information, and service. For domestic brands, sentiment index for 'safety' was the highest. For overseas brands, 'entertainment' was the most positive UX factor.

• Science of Emotion and Sensibility
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• v.16 no.2
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• pp.167-176
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• 2013

User-created text data are increasing rapidly caused by development of social media. In opinion mining, User's opinions are extracted by analyzing user's text. A primary goal of sentiment analysis as a branch of opinion mining is to extract user's opinions from a text that is required to build a list of emotion terms. In this paper, we built a list of emotion terms to analyse a sentiment of social media using Facebook as a representative social media. We collected data from Facebook and selected a emotion terms, and measured the dimensions of valence and activation through a survey. As a result, we built a list of 267 emotion terms including the dimension of valence and activation.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.13-15
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• 2021

텍스트에 나타나는 감성을 분석하는 NLP task 중 하나인 감성분석에 자주 사용되는 한국어와 외국어 데이터들에 대해 다양한 BERT 모델들을 적용한 결과를 고성능 순서로 정리한 사이트(Paper with code)와 Github를 통해 준수한 성능을 보이는 BERT 모델들을 분석하고 실행해보며 성능향상을 통한 차별성을 가지는 것이 목표이다.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.91-117
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• 2022

The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.181-188
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• 2020

In this study, we propose a comparative study to confirm the impact of various word embedding techniques on the performance of sentiment analysis. Sentiment analysis is one of opinion mining techniques to identify and extract subjective information from text using natural language processing and can be used to classify the sentiment of product reviews or comments. Since sentiment can be classified as either positive or negative, it can be considered one of the general classification problems. For sentiment analysis, the text must be converted into a language that can be recognized by a computer. Therefore, text such as a word or document is transformed into a vector in natural language processing called word embedding. Various techniques, such as Bag of Words, TF-IDF, and Word2Vec are used as word embedding techniques. Until now, there have not been many studies on word embedding techniques suitable for emotional analysis. In this study, among various word embedding techniques, Bag of Words, TF-IDF, and Word2Vec are used to compare and analyze the performance of movie review sentiment analysis. The research data set for this study is the IMDB data set, which is widely used in text mining. As a result, it was found that the performance of TF-IDF and Bag of Words was superior to that of Word2Vec and TF-IDF performed better than Bag of Words, but the difference was not very significant.

• Journal of Wetlands Research
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• v.23 no.4
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• pp.364-371
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• 2021

Public awareness of alien species can vary by generation, period, or specific events associated with these species. An understanding of public awareness is important for the management of alien species because differences in public awareness can affect the establishment and implementation of management plans. We analyzed digital texts on social media platforms, news articles, and internet search volumes used in conservation culturomics to understand public interest and sentiment regarding alien freshwater species. The number of tweets, number of news articles, and relative search volume to 11 freshwater alien species were extracted to determine public interest. Additionally, the trend over time, seasonal variability, and repetition period of these data were confirmed. We also calculated the sentiment score and analyzed public sentiment in the collected data using sentiment analysis based on text mining techniques. The American bullfrog, nutria, bluegill, and largemouth bass drew relatively more public interest than other species. Some species showed repeated patterns in the number of Twitter posts, media coverage, and internet searches found according to the specified periods. The text mining analysis results showed negative sentiments from most people regarding alien freshwater species. Particularly, negative sentiments increased over the years after alien species were designated as ecologically disturbing species.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.480-484
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• 2022

텍스트 분류는 입력받은 텍스트가 어느 종류의 범주에 속하는지 구분하는 것이다. 분류 모델에 있어서 좋은 성능을 나타내기 위해서는 충분한 양의 데이터 셋이 필요함을 많은 연구에서 보이고 있다. 이에 따라 데이터 증강기법을 소개하는 많은 연구가 진행되었지만, 실제로 사용하기 위한 모델에 곧바로 적용하기에는 여러 가지 문제점들이 존재한다. 본 논문에서는 데이터 증강을 위해 스타일 변환 기법을 이용하였고, 그 결과 기존 방법 대비 한국어 감성 분류의 성능을 높였다.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.153-160
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• 2021

With the advent of the "age of video" due to the simplification of video content production and the convenience of broadcasting channel operation, review videos on various products are drawing attention. We proposes RASIA, an integrated reliability analysis system based on verbal and nonverbal sentiment analysis of review videos. RASIA extracts and quantifies each emotional value obtained through language sentiment analysis and facial analysis of the reviewer in the video. Subsequently, we conduct an integrated reliability analysis of standardized verbal and nonverbal sentimental values. RASIA provide an new objective indicator to evaluate the reliability of the review video.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06d
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• pp.153-155
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• 2011

최근 음악 검색 연구에서 일반적으로 사용되는 방법은 키워드 중심의 텍스트 기반 검색방식, 음원의 특징 정보나 허밍 질의 처리 등을 이용하는 내용기반 검색 방식 등이 있다. 그러나 이러한 검색 방식은 단순히 원하는 음악을 질의에 따라 검색해 주며 인간의 감성을 고려하지 못하고 있다. 따라서 본 논문에서는 질의에 의한 검색뿐만 아니라 질의한 음원과 감성정도가 같은 음원을 추천하는 인간 감성 기반 음악 검색 및 추천 시스템을 제안한다. 인간 감성 기반 음악 검색 및 추천 시스템은 크게 2가지 요소로 구성된다. 첫 번째는 사용자가 질의한 질의어를 분석하는 감성기반 검색추론엔진과 두 번째는 음원의 특징 정보 및 감성 정보를 가지고 있는 음원 감성 정보 데이터베이스로 구성된다. 사용자의 감성에 따라 음악을 검색하고 추천한다는 것은 향후 음반 산업에 큰 발전에 기여할 것으로 기대한다.