A Comprehensive Survey on Sentiment Analysis Techniques
Sentiment analysis is a natural language processing (NLP) technique used to decide if the underlying sentiment is positive, negative, or neutral. Subjective information from the text can be extracted using sentiment analysis by recognizing its context and position. Data from a variety of sources...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2023-10-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/6632 |
| Summary: | Sentiment analysis is a natural language processing (NLP)
technique used to decide if the underlying sentiment is positive, negative, or
neutral. Subjective information from the text can be extracted using sentiment
analysis by recognizing its context and position. Data from a variety of
sources, like social network comments, news stories, consumer reviews, and
more, can be used for sentiment analysis. Sentiment analysis uses different
algorithms to analyze words, phrases, and context available in text and different
procedures to determine the overall sentiment communicated. There are various
ways in which sentiment analysis is performed, ranging from rule-based methods
that use lists of positive and negative terms as labeled data for training
machine learning algorithms to building classifiers. Understanding social
sentiment, underlying intents, and responses to various characteristics of
humans can be done with the help of sentiment analysis, which helps in
decision-making. The primary goal of this work is to provide the audience with
the knowledge needed to understand sentiment analysis, highlight potential
opportunities and challenges, and investigate recent studies that have been
published in reputable resources focusing on the field of sentiment analysis in
NLP. |
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| ISSN: | 2086-9614 2087-2100 |