Tweet Sentiment Analysis - IT Assignment Help

Assignment Task

Introduction
Sentiment analysis1 is an application of Natural Language Processing (a branch of Artificial Intelligence) that is concerned with detecting the sentiment of text. A common dimension for measuring sentiment uses labels positive, negative and neutral; there are many other possibilities as well (e.g. how strong the sentiment is, how active vs subdued it is, etc). two sample tweets about the current series Falcon and the Winter Soldier, one positive and one negative. Social media is a particularly popular arena for deploying sentiment analysis: companies want to know how their products are being perceived, etc. Consequently, there are many organisations oering apps or services for building them; a screenshot from a demo of such an app is given.
The earliest and simplest techniques for carrying out sentiment analysis (although this type of approach is still in fact widely used) just carried out keyword matching in the text, based on words from a source of words that have known sentiment (a sentiment lexicon). Often, these lexi-cons don't have extensive coverage: there are many words with sentiment that aren't included in them, particularly in the case of social media text, where misspellings, abbreviations and slang are common. Consequently, there are other approaches to the task: there's a large class of ma- chine learning3 techniques applied, as well as other techniques like label propagation,4 where
sentiment labels are propagated through a graph structure.
In this assignment, you'll work with a set of real tweets collected by researchers who developed one of the first approaches to sentiment analysis of tweets,5 and build your own tweet sentiment analyser. Early stages of the assignment just use a keyword-based approach, building up to a simple version of label propagation later.

Your Tasks
For your tasks, you'll be adding attributes and methods to existing classes given in the code bundle accompanying these specs. Where it's given, you should use exactly the method stub provided for implementing your tasks. Don't change the names or the parameters. You can add more functions if you like.
The two classes provided are Tweet and TweetCollection. The former represents an individual tweet, and the latter a collection of them.

Note that the Tweet class contains two enumerated types: Polarity represents the possible sentiment polarity values for a tweet (POSitive, NEGative, NEUTral or NONE); and Strength, for the strength of polarity (WEAK, STRONG), for the Distinction-level tasks.

This IT Assignment has been solved by our IT experts at My Uni Paper. Our Assignment Writing Experts are efficient to provide a fresh solution to this question. We are serving more than 10000+ Students in Australia, UK & US by helping them to score HD in their academics. Our Experts are well trained to follow all marking rubrics & referencing style.
Be it a used or new solution, the quality of the work submitted by our assignment experts remains unhampered. You may continue to expect the same or even better quality with the used and new assignment solution files respectively. There’s one thing to be noticed that you could choose one between the two and acquire an HD either way. You could choose a new assignment solution file to get yourself an exclusive, plagiarism (with free Turnitin file), expert quality assignment or order an old solution file that was considered worthy of the highest distinction.