Research Article
Lexicon-Based Sentiment Analysis Using Inset Dictionary: A Systematic Literature Review
@INPROCEEDINGS{10.4108/eai.5-10-2022.2327474, author={Asy Syifaur Roisah Rufaida and Adhistya Erna Permanasari and Noor Akhmad Setiawan}, title={Lexicon-Based Sentiment Analysis Using Inset Dictionary: A Systematic Literature Review}, proceedings={Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia}, publisher={EAI}, proceedings_a={ICAE}, year={2023}, month={6}, keywords={sentiment analysis lexicon-based method indonesia sentiment lexicon}, doi={10.4108/eai.5-10-2022.2327474} }- Asy Syifaur Roisah Rufaida
Adhistya Erna Permanasari
Noor Akhmad Setiawan
Year: 2023
Lexicon-Based Sentiment Analysis Using Inset Dictionary: A Systematic Literature Review
ICAE
EAI
DOI: 10.4108/eai.5-10-2022.2327474
Abstract
Sentiment analysis is one of the most exciting topics to research in text mining. Lexicon-based sentiment analysis produces good results across a wide range of conversational topics, is easily improved using various sources of information, and does not require additional training. However, only a few review papers have addressed sentiment analysis with specific lexicon dictionaries. As a result, neither data sources nor pre-processing techniques specific to a lexicon dictionary are discussed further. A Systematic Literature Review was conducted to provide a comprehensive and structured lexicon-based sentiment analysis using the Inset dictionary. The literature review resulted in selecting seventeen papers for a detailed study. The findings show that in the last five years, most sentiment analysis research using Inset focused on the health domain. Twitter provides the majority of the data for sentiment analysis. Stopword removal, tokenization, case folding, and stemming are common pre-processing techniques. This study also contained some additional observations from completed research.
