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, Vol. 17, No. 5, 2019.
DOI: https://doi.org/10.6025/jdim/2019/17/5/296-305
Link: http://dline.info/fpaper/jdim/v17i5/jdimv17i5_4.pdf
Abstract:
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: dictionary based 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.