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 $k$-nearest neighbors algorithm for the classification. Find the optimal value of $k$ 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.