User Guide for window users

1. Install Anaconda

from https://www.continuum.io/downloads

2. Install required python moduls

Change your working directory to sentiment_analysis folder which contains requirement.txt
> pip install -r requirement.txt

3. Make sure your local machine has install 64bit java

4. Download stanford corenlp

http://nlp.stanford.edu/software/stanford-corenlp-full-2016-10-31.zip

5. Start running stanford corenlp localhost server on your machine

Unzip the stanford corenlp

Change your working directory to the stanford-corenlp-full-2016-10-31 folder
> cd stanford-corenlp-full-2016-10-31

Make sure the current directory is the folder that contains all the *.jar files

Run the localhost server on port 9000
> java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 50000

6. Download required nltk packages

> ipython
> import nltk
> nltk.download()

In 'All Packages' tab, download WordNet, SentiWordNet, Punkt Tokenizer Model, WordNet-InfoContent

7. Do sentiment analysis

> python sentiment_analysis.py [input_corpus] [output_directory] [set_size]

[input_corpus]: review corpus to be analyzed, can be downloaded from https://snap.stanford.edu/data/web-Amazon.html
[output_directory]: directory that will contain all the *.txt output results, need to create the output folder by yourself before running the script
[set_size]: the number of reviews that user want to analyze from the entire input_corpus

Example:

> python sentiment_analysis.py .\Cell_Phones___Accessories.txt .\result\ 2000

8. Evaluate performance

Create a blank gold standard template first
> python evaluate_sentiment.py init [result] [evalset size] [output_init]

[result]: path to output_sentiment.txt produced by sentiment_analysis.py
[evalset size]: set size of gold standard
[output_init]: filename of gold standard output

Example:
> python evaluate_sentiment.py init .\result\output_sentiment.txt 200 goldstandard.txt

Manually annotate the gold standard

Evaluate the system output to the gold standard
> python evaluate_sentiment.py eval [goldstandard] [result] [output_eval] [detailed, optional]

[goldstandard]:	path of manually annotated goldstandard
[output_eval]: filename of evaluation result output

Example:
> python evaluate_sentiment.py eval goldstandard.txt .\result\output_sentiment.txt evaluation.txt detailed

9. Reference

Automatically Detecting and Rating Product Aspects from Textual Customer Reviews, Wouter Bancken, Daniele Alfarone and Jesse Davis, 2014