used NLTK to classify movie reviews as positive or negative(sentiment analysis hw for nlp course).
2. In Terminal clone from GH, 'git clone https://github.com/mohanenm/movie-classifier'
You will be working with movie review data, which you can download here:https://github.com/dennybritz/cnn-text-classification-tf/tree/master/data/rt-polaritydata
- find two files there (one with positive and one with negative reviews).
- build a binary classifier that will perform movie review classifica-tion automatically.
- 1.split data randomly into training (70%), development (15%)and test (15%) sets. (10 points)
- 2.Download and install LibSVM from https://www.csie.ntu.edu.tw/~cjlin/libsvm/. Convert the sentiment data into LibSVM sparse format. (30points) find some guidelines by following these links:https://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#/Q03:_Data_preparationhttps://stats.stackexchange.com/questions/61328/libsvm-data-format
- 3.Use LibSVM command-line tools such as svm-train and svm-predict totrain and evaluate a linear SVM model on your development set. Tune alinear SVM by trying a few different C values such as 0.0001, 0.001, . . . ,1000, 10000 on thedevelopmentset. Report the accuracy you obtainedfor each model. (30 points)
- 4.Evaluate your best model on the test set. Report the final accuracy ofyour model on thetestset. (30 points)