This research integrates Discourse Simplification (DisSim) into aspect-based sentiment analysis (ABSA) to improve aspect selection and prediction in complex financial texts, particularly central bank communications. The study focuses on decomposing Federal Open Market Committee (FOMC) minutes simple, canonical structures identify key sentences encapsulating the core messages of intricate narratives. investigation examines whether hierarchical segmenting texts can enhance ABSA performance...

This research analyzes media sentiment towards China and Korea with in-depth details. The study employs advanced Natural Language Processing (NLP) analysis methods to analyze 11,598 documents, including news articles reports. Through the analyses, detects patterns that reflect complex geopolitical economic interactions between two nations. uses transformer-based models for accurate detection. findings provide insights into how narratives may shape international perceptions. Importantly,...

In this research project, we used the financial texts published by Federal Open Market Committee (FOMC), known as FOMC Minutes, for sentiment analysis. The pre-trained FinBERT model, a state-of-the-art transformer-based model trained NLP tasks in finance, was utilized that. focus of has been on improving predictive performance complex sentences, our problem analysis shown that such sentences pose significant challenge to existing models. To accomplish objective original fine-tuned...

This research article analyzes the language used in official statements released by Federal Open Market Committee (FOMC) after its scheduled meetings to gain insights into impact of FOMC on financial markets and economic forecasting. The study reveals that is careful avoid expressing emotion their sentences follows a set templates cover situations. analysis employs advanced modeling techniques such as VADER FinBERT, trial test with GPT-4. results show FinBERT outperforms other predicting...

A deep learning methodology was utilized to predict the bulb weights of garlic and onions in Jeolla Province Korea. The Korea Rural Economic Institute (KREI) operates Outlook & Agricultural Statistics Information System (OASIS) platform, which provides actual measurements onions. We trained Neural Prophet (NP) lagged time-series model using this data. NP effectively handles variables their covariates by inserting a hidden layer. Our results indicate that performed with around 5% mean...

This study compares the sentiment detection capabilities of language models for domain central bank communication, particularly official statements released by U.S. Federal Open Market Committee (FOMC). While previous studies have explored FOMC this work is one few that use a natural processing-based approach. The analysis employs VADER, FinBERT, fine-tuned FinBERT model (FinBERT-FOMC), GPT-4, and Llama-3-70B.

This study investigates the impact of negative words on sentiment analysis and its effect South Korean stock market index, KOSPI200. The research analyzes a dataset 45,723 daily economic news articles using Word2Vec, cosine similarity, an expanded lexicon. findings suggest that incorporating significantly increases scores' negativity in titles, which can affect index. reveals augmented lexicon (Sent1000), including top 1,000 with high similarity to 'Crisis,' more effectively captures index...

Our study analyzed the impact of African swine fever (ASF) news on Korean meat market using sentiment analysis. We applied a neural network language model (NNLM) to generate index indicating whether had positive or negative consumer expectations. 24,143 articles estimate impulse responses price variables shocks. contributes significantly agricultural economics as it applies NNLM index. The empirical results indicated that ASF has substantial prices in Korea, and there is evidence...

The meat market behavior has been studied typically based on the price and income effect. Recently, unstructured data have generated from big analysis. Therefore, our study generates a sentiment index about African Swine Fever(ASF) estimates impact of change in ASF Korean market. Word2Vec technique, one Neural Network Language Model(NNLM), is used to generate index. Moreover, we decompose into its positive negative changes. Using this index, build non-linear Autoregressive Distributed...

본 연구는 2003년∼2019년 기간 우리나라 광역자치단체의 패널 자료를 이용해 생산과 분배소득 간 갭이 민간소비와 주택가격에 미치는 영향을 분석한다. 분석결과, 첫째, 소득 민간소비에 영향은 불명확하지만, 주택가격에는 양의 것으로 나타난다. 둘째, 갭은 순유입지역의 미치고, 순유출지역에는 음의 셋째, 순유출지역에만 이상의 분석결과는 소득의 역외 유출입이 해당 지역의 내수로 이어지지 않고, 주택가격 상승으로만 이어졌을 가능성이 높다는 것을 시사한다. 또한 역외유출(유입)이 장기간 지속될 경우 지역간 불균형과 경제적 비효율성은 보다 악화될 매우 알 수 있다.