The document outlines the phases of a major project involving analyzing product reviews. Phase 1 involved selecting a problem statement and collecting review data. Phase 2 involved preprocessing the data by removing stop words and stemming words. Features were then extracted. Phase 3 involved implementing k-means clustering on the normalized feature set to group similar reviews and analyze the results. Porter stemming and k-means clustering are also briefly defined.

1. Major Project

2.  Overall reviews of a product based on their ratings and content
of reviews
 Clustering of similar reviews of a product for sentiment analysis
 Checking the sentiment of reviews corresponding to their
ratings.

4.  Phase 1: In this we,
 We selected the desired problem statement
 Collected the required data set to analyse and operate upon
 Since the review of each product in the dataset was in the form
JSON Objects, it was converted to CSV format for analysis.

5.  Phase 2: This phase largely involves the data pre-processing.
 Firstly we removed the stop words from the dataset to reduce
the size of inverted list being formed.
 Next we applied the data cleaning so further reduce any useless
data.
 Porter – Stemming Algorithm is applied to further reduce our
dataset and now we can operate on the resulting data.
 We compared the sentiment of each word to form a rough idea
about the type of comments we getting corresponding to the
product.

6.  Phase 3: Implementation of the mining algorithm
 We extracted features from the resulting dataset in the above phase.
These features will now be the basis for implementation of the mining
algorithms in the next few steps.
 Features set is normalized to be used in cluster formation
 We check for Correlation on the normalized data and check for any
possibility of data-set reduction
 We applied the K-Means algorithm by using the features extracted
such as very positive, positive, neutral, negative, very negative as the
basis.
 The resultant data is then analyzed and its accuracy is checked for
completion of the project.

7.  Porter Stemming Algorithm
Stemming is a part of process of data cleaning. It is used to minimize
our dataset while creating posting list so that different words having same
root words are clubbed together as a single word in our posting list.
 K-Means Clustering
The data mining algorithm which is used to form clusters of reviews
having similar features. We will be creating 2 clusters in our project.