Homework

HW3: Vector Space Models

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:

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

[Label]\t[Document]

Below are the explanations of what each label signifies:

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

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

Last updated 13 days ago

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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