Corpus-Based Techniques for Sentiment Lexicon Generation: A Review
Mohammad Darwich, Shahrul Azman Mohd Noah, Nazlia Omar, Nurul Aida Osman · Journal of Digital Information Management · 2019
State-of-the-art sentiment analysis systems rely on a sentiment lexicon, which is the most essential feature that drives their performance.This resource is indispensable for, and greatly contributes to, sentiment analysis tasks.This is evident in the emergence of a large volume of research devoted to the development of automated sentiment lexicon generation algorithms.The task of tagging subjective words with a semantic orientation comprises two core approaches: dictionarybased and corpus-based.The former involves making use of an online dictionary to tag words, while the latter relies on co-occurrence statistics or syntactic patterns embedded in text corpora.The end result is a linguistic resource comprising a priori information about words, across the semantic dimension of sentiment.This paper provides a survey on the most prominent research works that utilize corpus-based techniques for sentiment lexicon generation.We also conduct a comparative analysis on the performance of state-of-the-art algorithms proposed for this task, and shed light on the current progress and challenges in this area.