• Annual Conference of KIPS
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• 2015.10a
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• pp.1333-1336
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• 2015

감성분석은 최근 오피니언 마이닝에서 주목받고 있는 분야로써, 특정 주제, 상품, 유명인사 등에 대한 사람들의 반응을 긍정 또는 부정으로 구분하거나 점수를 이용하여 긍정 또는 부정의 강도를 분석하는데 이용되고 있다. PMI(pointwise mutual information)와 SO-PMI(semantic orientation from pointwise mutual information)는 비교적 빠르고 간편하게 극성을 판단할 수 있다는 장점이 있지만, 어휘와 기준 어휘 사이의 극성 값이 넓은 범위를 갖는다는 단점이 있다. 본 논문에서는 일상적인 언어 사용 환경에서 나타나는 어휘로부터 감성을 분석하고자 하였다. 특히 어휘의 극성 값 편차로 인해 나타날 수 있는 어려움을 보완하기 위해 NPMI(normalized pointwise mutual information)를 이용하여 어휘의 감성을 분석하였다. PMI와 NPMI를 비교 분석한 결과 어휘의 감성 강도를 나타내는 데 있어서 밀집도에서 큰 차이를 보였다.

• Journal of Korean Society of Rural Planning
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• v.23 no.3
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• pp.37-51
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• 2017

There has been a growing concern on ontology especially in recent knowledge-based industry and defining a field-customized semantic word network is essential for building it. In this paper, a word network for ontology is established with 785 publications of Korean Society of Rural Planning(KSRP), from 1995 to 2017. Semantic relationships between words in the publications were quantitatively measured with the 'normalized pointwise mutual information' based on the information theory. Appearance and co-appearance frequencies of nouns and adjectives in phrases are analyzed based on the assumption that a 'noun phrase' represents a single 'concept'. The word network of KSRP was compared with that of $WordNet^{TM}$, a world-wide thesaurus network, for the verification. It is proved that the KSRP's word network, established in this paper, provides words' semantic relationships based on the common concepts of Korean rural planning research field. With the results, it is expecting that the established word network can present more opportunity for preparation of the fourth industrial revolution to the field of the Korean rural planning.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.1-7
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• 2024

Comparing text features involves evaluating the "similarity" between texts. It is crucial to use appropriate similarity measures when comparing similarities. This study utilized various techniques to assess the similarities between newspaper articles, including deep learning and a previously proposed method: a combination of Pointwise Mutual Information (PMI) and Word Pair Matching (WPM), denoted as PMI+WPM. For performance comparison, law data from medical research in Japan were utilized as validation data in evaluating the PMI+WPM method. The distribution of similarities in text data varies depending on the evaluation technique and genre, as revealed by the comparative analysis. For newspaper data, non-deep learning methods demonstrated better similarity evaluation accuracy than deep learning methods. Additionally, evaluating similarities in law data is more challenging than in newspaper articles. Despite deep learning being the prevalent method for evaluating textual similarities, this study demonstrates that non-deep learning methods can be effective regarding Japanese-based texts.

• Journal of Digital Convergence
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• v.18 no.5
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• pp.75-87
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• 2020

The present study determined new value that can be created through the convergence between artificial intelligence technology (AIT) and all industries by deriving and thoroughly analyzing major issues related to artificial intelligence (AI). This study analyzes domestic articles related to AI using topic modeling method based on LDA algorithm. Keywords were extracted from 3,889 articles of eleven metropolitan newspapers, eight business newspapers and major broadcasting companies; articles were selected by searching for the keyword "artificial intelligence". Keywords were extracted by optimizing the relevance parameter λ to improve the measure of pointwise mutual information (PMI), which shows the association among the keywords of each topic, and topic names were inferred from keywords based on valid evidence. The extracted topics widely showed changes occurring throughout society, economy, industries, culture, and the support policy and vision of the government.

• Annual Conference of KIPS
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• 2017.04a
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• pp.869-872
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• 2017

정보검색(Information retrieval) 및 텍스트 분석을 위해 수집하는 비정형 데이터 즉, 자연어를 전처리하는 과정 중 하나인 불용어(Stopword) 제거는 모델의 품질을 높일 수 있는 쉽고, 효과적인 방법 중에 하나이다. 특히 다양한 텍스트 문서에 잠재된 주제를 추출하는 기법인 토픽모델링의 경우, 너무 오래되거나, 수집된 문서의 도메인이나 성격과 무관한 불용어의 제거로 인해, 해당 토픽 모델에서 학습되어 생성된 주제 관련 단어들의 일관성이 떨어지게 된다. 따라서 분석가가 분류된 주제를 올바르게 해석하는데 있어 많은 어려움이 따르게 된다. 본 논문에서는 이러한 문제점을 해결하기 위해 일반적으로 사용되는 표준 불용어 대신 관련 도메인 문서로부터 추출되는 점별 상호정보량(PMI: Pointwise Mutual Information)을 이용하여 불용어를 자동으로 생성해주는 기법을 제안한다. 생성된 불용어와 표준 불용어를 통해 토픽 모델의 품질을 혼잡도(Perplexity)로써 측정한 결과, 본 논문에서 제안한 기법으로 생성한 30개의 불용어가 421개의 표준 불용어보다 더 높은 모델 성능을 보였다.

• Journal of KIISE:Software and Applications
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• v.37 no.6
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• pp.470-479
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• 2010

In relation to the extraction of opinion words, it may be difficult to directly apply most of the methods suggested in existing English studies to the Korean language. Additionally, the manual method suggested by studies in Korea poses a problem with the extraction of opinion words in that it takes a long time. In addition, English thesaurus-based extraction of Korean opinion words leaves a challenge to reconsider the deterioration of precision attributed to the one to one mismatching between Korean and English words. Studies based on Korean phrase analyzers may potentially fail due to the fact that they select opinion words with a low level of frequency. Therefore, this study will suggest the k-Structure (k=5 or 8) method, which may possibly improve the precision while mutually complementing existing studies in Korea, in automatically extracting opinion words from a simple sentence in a given Korean product review. A simple sentence is defined to be composed of at least 3 words, i.e., a sentence including an opinion word in ${\pm}2$ distance from the attribute name (e.g., the 'battery' of a camera) of a evaluated product (e.g., a 'camera'). In the performance experiment, the precision of those opinion words for 8 previously given attribute names were automatically extracted and estimated for 1,868 product reviews collected from major domestic shopping malls, by using k-Structure. The results showed that k=5 led to a recall of 79.0% and a precision of 87.0%; while k=8 led to a recall of 92.35% and a precision of 89.3%. Also, a test was conducted using PMI-IR (Pointwise Mutual Information - Information Retrieval) out of those methods suggested in English studies, which resulted in a recall of 55% and a precision of 57%.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.112-119
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• 2021

Social media has become a global means of communication in people's lives. Most people are using Twitter for communication purposes and its inappropriate use, which has negative effects on people's lives. One of the widely common misuses of Twitter is cyberbullying. As the resources of dialectal Arabic are rare, so for cyberbullying most people are using dialectal Arabic. For this reason, the ultimate goal of this study is to detect and classify cyberbullying on Twitter in the Arabic context in Saudi Arabia. To help in the detection and classification of tweets, Pointwise Mutual Information (PMI) to generate a lexicon, and Support Vector Machine (SVM) algorithms are used. The evaluation is performed on both methods in terms of the F1-score. However, the F1-score after applying the PMI is 50%, while after the SVM application on the resampling data it is 82%. The analysis of the results shows that the SVM algorithm outperforms better.

• Phonetics and Speech Sciences
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• v.16 no.3
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• pp.1-6
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• 2024

Vowels are fundamental elements of spoken language, providing insights into linguistic patterns and phonological systems. This study examines vowel inventories from 913 languages in a database, analyzing their statistical distributions. It investigates unique vowels within each language category, focusing on the two major dimensions of vowel height and backness, including diphthongs. The results show that vowel phonemes without diacritics constitute 30% of all distinct vowel types but account for 64% of the total vowel phonemes, highlighting the dominance of primary vowel articulations. The most frequent vowels are /i/, followed by /u/, /a/, /o/, /e/, /ɛ/, and /ɔ/. Multidimensional scaling of vowels, with or without diacritics, reveals distinctive clusters and co-occurrence patterns, necessitating more detailed analysis. Future research should consider the establishment of linguistic criteria on vowel representation and incorporate actual speech data for comprehensive linguistic studies.