Tweet Sentiment Analysis - Computer Science Assignment Help

Assignment Task

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). Figure 1 contains 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 offering apps or services for building them; a screenshot from a demo of such an app is given in Figure 2. 2 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 lexicons 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 machine 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.

T1 You will choose approprate representations for the Tweet class. You may or may not choose to base it on other classes I’ve supplied (Vertex, VertexIDList). Material from weeks 9–11 of lectures will be particularly relevant in helping you decide. You’ll need to write a constructor based on your chosen representation that instantiates an empty tweet.

T2 You’ll also need to do the same for the TweetCollection class. You might want to look ahead at the Credit-level tasks, which require the class to have some graph-like properties, to make the decision here. (Alternatively, you can just start with some underlying representation that will let you implement all of the Pass-level task functions, and then revise later.)

T3 Write some getter functions for the properties of the tweet passed in via the constructor, implemented using your chosen representation for tweets. Also write a getter and setter function for predicted polarity, which you will use when trying to predict the polarity of a tweet based on the content of its text.

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