- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
A
C
D
G
M
N
R
S
X
- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
What is sentiment analysis?
Sentiment analysis, also known as opinion mining, is a subfield of natural language processing (NLP) that identifies and extracts opinions.
Sentiment analysis is the interpretation and classification of positive, negative, and neutral sentiment within text data:
In the past, companies relied on traditional methods like surveys and focus groups to gather consumer feedback. However, it is now possible to analyze text from a variety of sources with greater accuracy and less effort thanks to machine learning and artificial intelligence technologies.
Examples of sentiment analysis:
Matching patients with a specialist to improve health outcomes
Movie reviews
Food reviews
Speech reviews
Brand monitoring
Market research
Customer feedback analysis
Why is sentiment analysis important?
Sentiment analysis helps gauge people's positive, negative, or neutral reactions to determine what people think. That information helps indicate if a product, service, or message needs to be adjusted to match an intended audience sentiment better.
Additional Resources
Tutorial: Natural Language Processing Tutorial - Sentiment Analysis
Blog post: Sentiment Analysis with VADER- Label the Unlabelled Data