UBR: User Bias Removal in Fine Grained Sentiment Analysis

Introduction

• 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.

Setup

Run "setup.sh" for setting up.

bash setup.sh

Testing

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 features

python lda_implement.py

For testing UBR-1 with tf-idf features

python tfidf.py 1

For testing UBR-2 with tf-idf faetures

python tfidf.py 2

Citation

@inproceedings{10.1145/3152494.3152520,
author = {Wadbude, Rahul and Gupta, Vivek and Mekala, Dheeraj and Karnick, Harish},
title = {User Bias Removal in Review Score Prediction},
year = {2018},
isbn = {9781450363419},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3152494.3152520},
doi = {10.1145/3152494.3152520},
booktitle = {Proceedings of the ACM India Joint International Conference on Data Science and Management of Data},
pages = {175–179},
numpages = {5},
keywords = {bias removal, score prediction, user modeling},
location = {Goa, India},
series = {CoDS-COMAD ’18}
}