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COMP9414: Artificial Intelligence
创建日期：2024-08-03 15:01:12
标签： COMP9414
Artificial Intelligence

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COMP9414: Artificial Intelligence


Assignment 2: Sentiment Analysis

Value: 25%

This assignment is inspired by a typical real-life scenario. Imagine you have been hired as a

Data Scientist by a major airline company. Your job is to analyse the Twitter feed to determine

customer sentiment towards your company and its competitors.

In this assignment, you will be given a collection of tweets about US airlines. The tweets have been

manually labelled for sentiment. Sentiment is categorized as either positive, negative or neutral.

Important: Do not distribute these tweets on the Internet, as this breaches Twitter’s

Terms of Service.

You are expected to assess various supervised machine learning methods using a variety of features

and settings to determine what methods work best for sentiment classification in this domain. The

assignment has two components: programming to produce a collection of models for sentiment

analysis, and a report to evaluate the effectiveness of the models. The programming part involves

development of Python code for data preprocessing of tweets and experimentation of methods using

NLP and machine learning toolkits. The report involves evaluating and comparing the models

using various metrics, and comparison of the machine learning models to a baseline method.

You will use the NLTK toolkit for basic language preprocessing, and scikit-learn for feature construction

and evaluating the machine learning models. You will be given an example of how to

use NLTK and scikit-learn for this assignment (example.py). For the sentiment analysis baseline,

NLTK includes a hand-crafted (crowdsourced) sentiment analyser, VADER,1 which may perform

well in this domain because of the way it uses emojis and other features of social media text to

intensify sentiment, however the accuracy of VADER is difficult to anticipate because: (i) crowdsourcing

is in general highly unreliable, and (ii) this dataset might not include much use of emojis

and other markers of sentiment.

Data and Methods

A training dataset is a tsv (tab separated values) file containing a number of tweets, with one

tweet per line, and linebreaks within tweets removed. Each line of the tsv file has three fields:

instance number, tweet text and sentiment (positive, negative or neutral). A test dataset is a tsv

file in the same format as the training dataset except that your code should ignore the sentiment

field. Training and test datasets can be drawn from a supplied file dataset.tsv (see below).

For all models except VADER, consider a tweet to be a collection of words, where a word is a string

of at least two letters, numbers or the symbols #, @, , $ or %, delimited by a space, after removing

all other characters (two characters is the default minimum word length for CountVectorizer in

scikit-learn). URLs should be treated as a space, so delimit words. Note that deleting “junk”

characters may create longer words that were previously separated by those characters.

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