- 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.
bash setup.sh
Scripts for testing is in three folders.
-
electronics
-
food
-
movies
cd to appropriate folder and then:
python doc2vec.py
python baseline.py #User mean,mode etc.
python predict5.py #Always predict 5
python lda_implement.py
python tfidf.py 1
python tfidf.py 2
@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}
}