• Phonetics and Speech Sciences
• /
• v.10 no.3
• /
• pp.9-18
• /
• 2018

This paper presents an acoustic and phonological study of the alveolar flaps in American English. Based on the Buckeye Corpus, the flapping tokens produced by twenty men are analyzed at both lexical and post-lexical levels. The data, analyzed with Pratt speech analysis, include duration, F2 and F3 in voicing during the flap, as well as duration, F1, F2, F3, and f0 in the adjacent vowels. The results provide evidence on two issues: (1) The different ways in which voiced and voiceless alveolar stops give rise to neutralized flapping stops by following lexical and post-lexical levels, (2) The extent to which the vowel features (height, frontness, and tenseness) affect flapping sounds. The results show that flaps are affected by pre-consonantal vowel features at the lexical as well as post-lexical levels. Unlike previous studies, this study uses the Praat method to distinguish flapped from unflapped tokens in the Buckeye Corpus and examines connections between the lexical and post-lexical levels.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.5
• /
• pp.546-550
• /
• 2010

In this paper, we propose a method for the postprocessing of cursive script recognition in Wine Label Images. The proposed method mainly consists of three steps: combination matrix generation, character combination filtering, string matching. Firstly, the combination matrix generation step detects all possible combinations from a recognition result for each of the pieces. Secondly, the unnecessary information in the combination matrix is removed by comparing with bigram of word in the lexicon. Finally, string matching step decides the identity of result as a best matched word in the lexicon based on the levenshtein distance. An experimental result shows that the recognition accuracy is 85.8%.

• Journal of Digital Contents Society
• /
• v.18 no.3
• /
• pp.585-592
• /
• 2017

As development of personal devices have made everyday use of internet much easier than before, it is getting generalized to find information and share it through the social media. In particular, communities specialized in each field have become so powerful that they can significantly influence our society. Finally, businesses and governments pay attentions to reflecting their opinions in their strategies. The stock market fluctuates with various factors of society. In order to consider social trends, many studies have tried making use of bigdata analysis on stock market researches as well as traditional approaches using buzz amount. In the example at the top, the studies using text data such as newspaper articles are being published. In this paper, we analyzed the post of 'Paxnet', a securities specialists' site, to supplement the limitation of the news. Based on this, we help researchers analyze the sentiment of investors by generating a domain-specific sentiment lexicon for the stock market.

• Smart Media Journal
• /
• v.13 no.6
• /
• pp.35-43
• /
• 2024

During the post-pandemic period, the interest in foreign language learning surged, leading to increased usage of language-learning apps. With the rising demand for these apps, analyzing app reviews becomes essential, as they provide valuable insights into user experiences and suggestions for improvement. This research focuses on extracting insights into users' opinions, sentiments, and overall satisfaction from reviews of HelloTalk, one of the most renowned language-learning apps. We employed topic modeling and emotion analysis approaches to analyze reviews collected from the Google Play Store. Several experiments were conducted to evaluate the performance of sentiment classification models with different settings. In addition, we identified dominant emotions and topics within the app reviews using feature importance analysis. The experimental results show that the Random Forest model with topics and emotions outperforms other approaches in accuracy, recall, and F1 score. The findings reveal that topics emphasizing language learning and community interactions, as well as the use of language learning tools and the learning experience, are prominent. Moreover, the emotions of 'admiration' and 'annoyance' emerge as significant factors across all models. This research highlights that incorporating emotion scores into the model and utilizing a broader range of emotion labels enhances model performance.

• Journal of KIISE:Software and Applications
• /
• v.26 no.5
• /
• pp.647-656
• /
• 1999

본 논문에서는 오프라인 무제약 필기 한글 단어를 인식하기 위한 시스템을 제안한다. 제안된 단어 인식 시스템은 크게 다석가지 모듈-문자 분리,조합행렬생성, 특징 추출, 문자인식, 사전 후처리 -로 구성되어 있다. 문자 분리 모듈은 입력된 단어 영상을 하나의 문자보다 더 작은 이미지 조각으로 과다 분리하며 , 조합 행렬 생성모듈에서는 동적 프로그래밍 기법을 이용하여 분리된 이미지 조각들로부터 사전상의 모든 단어들과 대응되는 가능한 모든 조합을 생성한다. 문자인식모듈은 각 그룹에 대하여 일괄적으로 얻어진 특징과 유니그램을 이용하여 문자인식을 수행한다. 마지막으로 사전 후처리 모듈에서는 각 그룹에 대한 문자인식 결과와 단어 사전을 사용하여 입력단어에 대한 최종 인식 결과를 도출한다. 본 문에서 제안한 방법은 문자 분리, 문자 인식 및 후처리를 상호 보완적으로 결합함으로써 한글이 포함된 무제약 필기 문자열을 효과적으로 인식할 수 있다. 제안된 시스템의 성능을 평가하기 위하여 실제 우편 봉투 상에 쓰여진 필기 한글 단어 200개를 대상으로 실험을 하였다. 실험 결과 200개의 단어중 172개의 단어를 정인식하여 86%의 정확도를 얻을 수 있었으며 나머지 28개의 오인식된 단어들을 분석한 결과 대부분의 오류는 문자 인식기의 낮은 신뢰도 때문임을 알 수 있었다. 또한, 하나의 단어를 인식하기 위하여 약 2초가 소요되었다.

• Information Systems Review
• /
• v.21 no.1
• /
• pp.91-111
• /
• 2019

Around the end of 2017, the investment fever for cryptocurrencies-especially Bitcoin-has started all over the world. Especially, South Korea has been at the center of this phenomenon. Sinceit was difficult to find the profitable investment opportunities, people have started to see the cryptocurrency markets as an alternative investment objects. However, the cryptocurrency fever inSouth Korea is mostly based on psychological phenomenon due to expectation of short-term profits and social atmosphere rather than intrinsic value of the assets. Therefore, this study aimed to analyze influence of people's social sentiment on price movement of cryptocurrency. The data was collected for 181 days from Nov 1st, 2017 to Apr 30th, 2018, especially focusing on Bitcoin-related post in Twitter along with price of Bitcoin in Bithumb/UPbit. After the collected data was refined into neutral, positive and negative words through sentiment analysis, the refined neutral, positive, and negative words were put into regression model in order to find out the impacts of social sentiments on Bitcoin price. After examining the relationship by the regression analyses and Granger Causality tests, we found that the positive sentiments had a positive relationship with Bitcoin price, while the negative words had a negative relation with it. Also, the causality test results show that there exist two-way causalities between social sentiment and Bitcoin price movement. Therefore, we were able to conclude that the Bitcoin investors'behaviors are affected by the changes of social sentiments.