.
Why are we even interested in Sentiment Analysis? Well, here’s situation to help you understand.
Basic Terminologies
subjective
objective
Polarity
pre-process
normalize
Precision
Recall
F1 Score
Sentiment Analysis using Lexicon Based Models
AFINN Lexicon
SentiWordNet
VADER
Classification of Sentiment with Supervised Learning
Text pre-processing and data normalization
Feature Engineering
Model Training, Prediction and Evaluation
Bag of Words Model- (BOW)
SVM model on BOW features
Term Frequency-Inverse Document Frequency (TF-IDF)
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https://github.com/AbhinandanRoul/Sentiment-Analysis--Lexicon-Models-vs-Machine-Learning
https://medium.com/nerd-for-tech/sentiment-analysis-lexicon-models-vs-machine-learning-b6e3af8fe746
