####A0176605B Wang Jia##### Lab 1: Microblog Clssification Deadline: 19 Feb 2018

Environment Setting

1. Python 3.6

Installation

1. nltk
2. simplejson
3. pickle
4. numpy
5. scipy
6. scikit-learn

Usage

Early Fusion Classifier

1. Run 'early_processor.py' to prepocess tweet attributes, including feature extraction and combination, data cleaning (e.g., remove url, punctuations, time), word tokenize, stemming, remove low-frequency words and stopping words.
2. Run 'early_classifier.py', which adopts a random forest classifier. You are supposed to see performances (classification score, average percision, average recall, f1 score) printing into the screen.

Late Fusion Classifier

1. Run 'late_processor.py' to prepocess tweet attributes, including data cleaning (e.g., remove url, punctuations, time), word tokenize, stemming, remove low-frequency words and stopping words. The three features(tweets texts, description, hashtag) will extracted and processed seperately.
2. Run 'late_classifier.py', which adopts 3 individual classifier and a combined model. You are supposed to see performances (classification score, average percision, average recall, f1 score) of the 3 single model and the final combined model printing into the screen.