TY - JOUR
T1 - A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research
T2 - Approaches, Datasets, and Future Research
AU - Tan, Kian Long
AU - Lee, Chin Poo
AU - Lim, Kian Ming
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/4
Y1 - 2023/4
N2 - Sentiment analysis is a critical subfield of natural language processing that focuses on categorizing text into three primary sentiments: positive, negative, and neutral. With the proliferation of online platforms where individuals can openly express their opinions and perspectives, it has become increasingly crucial for organizations to comprehend the underlying sentiments behind these opinions to make informed decisions. By comprehending the sentiments behind customers’ opinions and attitudes towards products and services, companies can improve customer satisfaction, increase brand reputation, and ultimately increase revenue. Additionally, sentiment analysis can be applied to political analysis to understand public opinion toward political parties, candidates, and policies. Sentiment analysis can also be used in the financial industry to analyze news articles and social media posts to predict stock prices and identify potential investment opportunities. This paper offers an overview of the latest advancements in sentiment analysis, including preprocessing techniques, feature extraction methods, classification techniques, widely used datasets, and experimental results. Furthermore, this paper delves into the challenges posed by sentiment analysis datasets and discusses some limitations and future research prospects of sentiment analysis. Given the importance of sentiment analysis, this paper provides valuable insights into the current state of the field and serves as a valuable resource for both researchers and practitioners. The information presented in this paper can inform stakeholders about the latest advancements in sentiment analysis and guide future research in the field.
AB - Sentiment analysis is a critical subfield of natural language processing that focuses on categorizing text into three primary sentiments: positive, negative, and neutral. With the proliferation of online platforms where individuals can openly express their opinions and perspectives, it has become increasingly crucial for organizations to comprehend the underlying sentiments behind these opinions to make informed decisions. By comprehending the sentiments behind customers’ opinions and attitudes towards products and services, companies can improve customer satisfaction, increase brand reputation, and ultimately increase revenue. Additionally, sentiment analysis can be applied to political analysis to understand public opinion toward political parties, candidates, and policies. Sentiment analysis can also be used in the financial industry to analyze news articles and social media posts to predict stock prices and identify potential investment opportunities. This paper offers an overview of the latest advancements in sentiment analysis, including preprocessing techniques, feature extraction methods, classification techniques, widely used datasets, and experimental results. Furthermore, this paper delves into the challenges posed by sentiment analysis datasets and discusses some limitations and future research prospects of sentiment analysis. Given the importance of sentiment analysis, this paper provides valuable insights into the current state of the field and serves as a valuable resource for both researchers and practitioners. The information presented in this paper can inform stakeholders about the latest advancements in sentiment analysis and guide future research in the field.
KW - advances
KW - deep learning
KW - ensemble learning
KW - machine learning
KW - review
KW - sentiment analysis
KW - survey
UR - http://www.scopus.com/inward/record.url?scp=85152701212&partnerID=8YFLogxK
U2 - 10.3390/app13074550
DO - 10.3390/app13074550
M3 - Review article
AN - SCOPUS:85152701212
SN - 2076-3417
VL - 13
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 7
M1 - 4550
ER -