Widely accepted steps for sentiment analysis. Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy

1. Steps in Sentimental analysis
1) Read the data
2) Build text Corpus
3) Data Transformation / Cleaning
4) Tag the sentiments
5) Checking the overall Sentiment Score
>table (analysis$score)
6) Analysis$sentiment<- ifelse(analysis$score>0,”positive”, ifelse
(analysis$score <0 , “negative”, “neutral”))
table(analysis$sentiment)
7. Clean the data again
8. Split the data into training & test data sets
9. Tagging the testing & training datasets
>train_data$type=“train”
>test_data$type=“test”
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2. Steps in Sentimental analysis
10. Combine Tweets<-rbind(train_data, test_data)
11. Building TDM matrix=create_matrix(………………..)
12. Convert to matrix data type mat=as.matrix(matrix)
13. Build the data to specify response variable, training set, testing set,
container=create_contrainer(……………….)
14. Train the model/create model using algorithm svm,RF,TREE…etc
15. Test the model > results =classify_model(container, models)
16. Model Performance – Confusion Matrix , Recall Accuracy.
17. Model summary- summary(analytics)
18. Ensemble of models – analystics@ensemble_summary
19. Cross Validation
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