Construction of an Inquiry Letter Sentiment Dictionary Using SO-PMI and Word2Vec for Sentiment Analysis

Sentiment analysis of financial texts provides valuable insights into market dynamics, but relies on domain-specific dictionaries.Inquiry letters as particular financial texts lack tailored sentiment dictionaries, limiting research in the important domain.This study develops an Inquiry Letter Sentiment Dictionary (ILSD) using innovative integration of semantic orientation pointwise mutual information (SO-PMI), word2vec, and manual screening.The ILSD leverages co-occurrence and contextual information to expand coverage and sentiment word capture capabilities beyond existing general and financial dictionaries.We collected 1754 inquiry letters from the Shanghai Stock Exchange (SSE).The experiment results indicate that the ILSD performs better than other sentiment dictionaries according to coverage, accuracy (ACC), F1 weighted score (F1Weighted), Matthews correlation (MCC), and geometric mean (G-mean), which proves the effectiveness of the ILSD in practice.