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Homework

Your task is to develop a sentiment analyzer train on the Stanford Sentiment Treebank:

  • Create a vector_space_models.py file in the src/homework/ directory.
  • Define a function named sentiment_analyzer() that takes two parameters, a list of training documents and a list of test documents for classification, and returns the predicted sentiment labels along with the respective similarity scores.
  • Use the kk-nearest neighbors algorithm for the classification. Find the optimal value of kk using the development set, and then hardcode this value into your function before submission.

Data​

The sentiment_treebank directory contains the following two files:

  • sst_trn.tst: a training set consisting of 8,544 labeled documents.
  • sst_dev.tst: a development set consisting of 1,101 labeled documents.

Each line is a document, which is formatted as follows:

[Label]\t[Document]

Below are the explanations of what each label signifies:

  • 0: Very negative
  • 1: Negative
  • 2: Neutral
  • 3: Positive
  • 4: Very positive

Submission​

Commit and push the vector_space_models.py file to your GitHub repository.

Extra Credit​

Define a function named sentiment_analyzer_extra() that gives an improved sentiment analyzer.

Rubric​

  • Code Submission (1 point)
  • Program Execution (1 point)
  • Development Set Accuracy (4 points)
  • Evaluation Set Accuracy (4 points)
  • Concept Quiz (2 points)