1. Tutorial for LightSIDE
June 4, 2018
Heejun Kim

2. The workflow for Text Mining
Preparing data
Extracting features
Building model
Predicting labels
Error analysis
Preparing Data: Movie Reviews, Positive vs. Negative
Whether a figure is a triangle or not. In my case, I am interested in predicting whether information is credible or not.
Bag of words representation
LightSIDE can cover all except the first step

3. Installing LightSIDE You should have JRE (1.8 preferred) or JDK
Download the zip file linked as “Program: LightSIDE” from the course website
Unzip the file
Mac: LightSide.app
Windows: LightSide.bat
Linux: run.sh
JRE is an acronym for java running environment which is a basically virtual machine that help you to run Java-based program.
Introduce where students can find the manual

4. Preparing Data (LightSIDE)
CSV file (comma delimited text file)
One column for text, another column for class
Additional attributes (e.g., length) that are pre-processed can be included in additional columns (will be read by using column features extractor)
Encoding: UTF-8 is recommended

5. Preparing Data (LightSIDE)

6. Open Files Open a file Select a file Check details
For the simple work flow for the 2nd assignment, you will only need to have first, third and last tab. So let me go over the core process first and get some question and explore more functions later.

7. Extract Features Select extractor Configure detailed option Execute
Check performance of features
Set threshold
For the second assignment, you are only going to use “Basic Features”. However, for the 3rd assignment, you may want to explore other feature extractors.
Only Unigram and select the “Skip stopwords in N-Grams” option except #5 question
LightSIDE allows you to set a threshold on the minimum number of training set instances that must contain a particular feature in order for that feature to make it into the feature representation. If we set t=2, then only terms that appear in at least 2 training set instances make into the feature representation.

8. An example of feature table

9. Building Model + Predicting Label
Select a machine learning algorithm (e.g., NaiveBayes, Logistic Regression )
Evaluation method: independent training/test data or n-fold cross validation for your project
Only Naïve Bayes algorithm

10. Building Model + Predicting Label
Training data + independent test data
Training data
Test data
Training set, validation set, testing set

11. Building Model + Predicting Label
Cross validation (e.g., 5 fold)
Test
data
Run
Accuracy
1st
0.78
2nd
0.76
3rd
0.77
4th
0.73
Over-fitting
5th
0.79
avg
0.766
Training
data

12. Build Models Configure detailed option Select algorithm
Select a feature table
Select evaluation option
Execute
Only Naïve Bayes algorithm. Options may be appropriate for working with numeric feature values, but are generally unimportant. In some cases, these configuration are important.
Sometimes using train.csv for training and testing, other times, using trains.csv for training and using test.csv for testing
Showing table in the assignment and explain what training set accuracy and testing set accuracy
Check performance of prediction

13. Select exploration method
Explore Results
Select case
Select a model
Select a feature
Select matrix
Select exploration method
Check the case
Click a case

14. Choose a file you want to make prediction for
Predict Labels
Export results
Select a model
Check results
Choose a file you want to make prediction for

15. Any questions?