Trip Advisor Dataset Loader

For the Review Graph Mining project, this package provides a loader of the Trip Advisor dataset provided by Dr. Wang.

Installation

Use pip to install this package.

$ pip install --upgrade rgmining-tripadvisor-dataset

Usage

This package provides module tripadvisor and this module provides load() function. The load function takes a graph object which implements the Graph protocol.

For example, the following code constructs a graph object provides the FRAUDAR algorithm, loads the Trip Advisor dataset, runs the algorithm, and then outputs names of anomalous reviewers. Since this dataset consists of huge reviews, loading may take long time.

import fraudar
import tripadvisor

# Construct a graph and load the dataset.
graph = fraudar.ReviewGraph()
tripadvisor.load(graph)

# Run the analyzing algorithm.
graph.update()

# Print names of reviewers who are judged as anomalous.
for r in graph.reviewers:
 if r.anomalous_score == 1:
   print r.name

# The number of reviewers the dataset has: -> 1169456.
len(graph.reviewers)

# The number of reviewers judged as anomalous: -> 147.
len([r for r in graph.reviewers if r.anomalous_score == 1])

Note that you may need to install the FRAUDAR algorithm for the Review Mining Project by

pip install rgmining-fraudar

API Reference

• tripadvisor package
  • load()
  • reviews()
  • Submodules
  • tripadvisor.cli module
  • tripadvisor.debug module
  • tripadvisor.loader module

License

This software is released under The GNU General Public License Version 3, see COPYING for more detail.

The authors of the Trip Advisor dataset, which this software imports, requires to cite the following papers when you publish research papers using this package:

• Hongning Wang, Yue Lu, and ChengXiang Zhai, “Latent Aspect Rating Analysis without Aspect Keyword Supervision,” In Proc. of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’2011), pp.618-626, 2011;

• Hongning Wang, Yue Lu, and Chengxiang Zhai, “Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach,” In Proc. of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’2010), pp.783-792, 2010.