Sentiment Analysis Project Proposal
I will utilize the Amazon product review database for the sentiment analysis project (Blitzer, Dredze, & Pereira, 2007). This data set contains reviews of products from four different categories. I will utilize the Support Vector Machine (SVM) algorithm in Python to analyze the data. According to Blitzer, Dredze, and Pereira, SVM is the best algorithm while Naïve Bayes is the least accurate, although both methods give close results. Therefore, it may be necessary for more than one algorithm to be utilized to test their theory.
After analyzing the data sets and completing the required clean-up, SVM will be utilized on the test and training sets of data. The text of each of the product reviews, as well as the rating (one to five stars, representing a scale of negative to positive), will be analyzed. The sentiment analysis will train a classifier to identify positive reviews and negative reviews, and then analyze the validity of the algorithm compared to the number of stars given by the reviewer.
One research questions is, “Can the sentiment algorithm correctly classify positive and negative reviews based on text”. This question will be answered by using SVM (or another algorithm, if necessary), on the training and testing sets. If SVM does not accurately identify the text, another method may be used. However, only with testing of the algorithm can this question be answered.
The final step in this project will be to reflect upon the choices made in preparing the data, choosing an algorithm, and testing the data. A report will be completed to discuss the choices made step-by-step, the results, and any possible issues with the research methodologies. Importantly, the report will give an in-depth analysis of the algorithm(s) used and how future research could be completed to enhance the project results.
Hello,
My name is Vlad Markov.
I'm "crafting" software more then 17 years.
I've just started working as a freelancer here, but I'm really skilled in that.
If you give me a chance, I will prove it on the excellent result.
Each project I divide to milestones like this:
1. Define project specifications.
2. Writing and approve documentation.
3. Prototyping and first demo with main functionality.
4. Finalizing project and second demo.
5. Fixing some issues/bugs and deploying project for customer.
For example I've added typical milestones to the project, but of course it could be changed for each project's requirements.
Actually, each milestone should be paid after completing.
Additionally, this topic is really interested for me as a programmer.
If you have some questions, please ask me.
Sincerely,
Vlad.
Hello,
So I in my summer project worked on sentiment Analysis using NLTK available in python, but to classify the sentiment we used BAYESIAN classifier and normalized it into a numerical rating between 1 to 5. We did it as a part of product review analysis and we used Cell Phone reviews available on amazon.com.
But doing it with more than one algorithm will take more than a week's time. And I hope you have sorted the reviews into 2 groups of positive and negatives because sorting it takes time.
So if you can wait for 10-12 days for completion of this project and have algorithm ready, I think I can do it.
When do you need this project called "need python developer" up and running?
or is this ongoing work?
I'm an experienced software engineer and designer.
7AM - 7PM EST
available on skype
available on phone
located in the US