Financial economic research has extensively documented the fact that the impact of the
arrival of negative news on stock prices is more intense than that of the arrival of positive …

In recent time, lexicon-based LSTM and pre-training language models are combined to
explore the Chinese Named Entity Recognition (NER) and achieve the current state-of-the …

The introduction of pre-trained large language models (LLMs) has transformed NLP by fine-
tuning task-specific datasets, enabling notable advancements in news classification …

Globalization has led to the widespread adoption of translated corporate annual reports in
international markets. Nonetheless, it remains largely unexplored whether these translated …

Implementing a healthcare monitoring system based on IoT technology can effectively tackle
a situation like a current epidemic. The IoT field has experienced significant growth in the …

The Bank Nifty Index is the primary subject of this research paper's in-depth analysis and
proposing a sentiment analysis model exclusively for the Indian stock market. The goal is to …

Recent attention has focused on the interplay between news media and stock prices,
prompted by extensive exploration of stock market dynamics. This study is designed to …

The research aims to investigate the impact of mergers and acquisitions (M&A) on the short-
term financial performance of acquirers in Europe. Despite the widespread use of M&A …

Abstract The purpose of Named Entity Recognition (NER) is to identify and mark entities with
specific meanings in a text. Compared with English NER, Chinese NER is blurry about the …