Exploiting semantic relationships for unsupervised expansion of sentiment lexicons
作者：
Highlights：
• We demonstrate that semantic relationships can be effective for lexicon expansion.
• We propose a novel method that explores distances between word embeddings.
• Our unsupervised method enhances lexicon coverage while keeping high precision.
• In our experiments, we beat all lexicon-expansion baselines by large margins.
• Our approach is competitive with pre-trained transformers (BERT) without any training.
摘要
•We demonstrate that semantic relationships can be effective for lexicon expansion.•We propose a novel method that explores distances between word embeddings.•Our unsupervised method enhances lexicon coverage while keeping high precision.•In our experiments, we beat all lexicon-expansion baselines by large margins.•Our approach is competitive with pre-trained transformers (BERT) without any training.
论文关键词：Sentiment analysis,Lexicon dictionary,Word embeddings,Lexicon expansion
论文评审过程：Received 16 January 2020, Revised 19 July 2020, Accepted 20 July 2020, Available online 29 July 2020, Version of Record 7 August 2020.
论文官网地址：https://doi.org/10.1016/j.is.2020.101606
