• Major problem in current sentiment classification models is noise due to presence of user biases in reviews rating.
• We worked on two simple statistical methods to remove user bias noise to improve fine grained sentimental classification.
• We applied our methods on SNAP published Amazon Fine Food Reviews data-set and two major categories Electronics and Movies & TV of e-Commerce Reviews data-set. Correspondingly, there are 3 folders, food, electronics and movies.

Run "setup.sh" for setting up.

Scripts for testing is in three folders.

• electronics

• food

• movies

cd to appropriate folder and then:

####For getting PV-DBoW features

python doc2vec.py

####For testing various baselines

python baseline.py #User mean,mode etc.

python predict5.py #Always predict 5

####For testing UBR-1 and UBR-2 with LDA faetures python lda_implement.py

####For testing UBR-1 with tf-idf faetures python tfidf.py 1

####For testing UBR-2 with tf-idf faetures python tfidf.py 2

• Rahul Kumar Wadbude (IIT Kanpur)(warahul@iitk.ac.in)
• Vivek Gupta (Microsoft Research)(t-vigu@microsoft.com)
• Dheeraj Mekala (IIT Kanpur)(dheerajm@iitk.ac.in)
• Janish Jindal (IIT Kanpur)(janish@iitk.ac.in)
• Harish Karnick (IIT Kanpur)(hk@iitk.ac.in)