A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research
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 in...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/7/4550 |
| _version_ | 1827747062139060224 |
|---|---|
| author | Kian Long Tan Chin Poo Lee Kian Ming Lim |
| author_facet | Kian Long Tan Chin Poo Lee Kian Ming Lim |
| author_sort | Kian Long Tan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:41:52Z |
| format | Article |
| id | doaj.art-62d35b386dff480b90825e73b9bedfa9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:41:52Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-62d35b386dff480b90825e73b9bedfa92023-11-17T16:21:58ZengMDPI AGApplied Sciences2076-34172023-04-01137455010.3390/app13074550A Survey of Sentiment Analysis: Approaches, Datasets, and Future ResearchKian Long Tan0Chin Poo Lee1Kian Ming Lim2Faculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaSentiment 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.https://www.mdpi.com/2076-3417/13/7/4550sentiment analysisreviewsurveyadvancesmachine learningdeep learning |
| spellingShingle | Kian Long Tan Chin Poo Lee Kian Ming Lim A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research Applied Sciences sentiment analysis review survey advances machine learning deep learning |
| title | A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research |
| title_full | A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research |
| title_fullStr | A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research |
| title_full_unstemmed | A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research |
| title_short | A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research |
| title_sort | survey of sentiment analysis approaches datasets and future research |
| topic | sentiment analysis review survey advances machine learning deep learning |
| url | https://www.mdpi.com/2076-3417/13/7/4550 |
| work_keys_str_mv | AT kianlongtan asurveyofsentimentanalysisapproachesdatasetsandfutureresearch AT chinpoolee asurveyofsentimentanalysisapproachesdatasetsandfutureresearch AT kianminglim asurveyofsentimentanalysisapproachesdatasetsandfutureresearch AT kianlongtan surveyofsentimentanalysisapproachesdatasetsandfutureresearch AT chinpoolee surveyofsentimentanalysisapproachesdatasetsandfutureresearch AT kianminglim surveyofsentimentanalysisapproachesdatasetsandfutureresearch |