Amazon Product Sentiment review

Apr 30, 2017Download as PPTX, PDF3 likes4,076 views

Lalit Jain

Using Gensim's Doc2Vec models (DM and DBOW) classify sentiments using Logistic, SVM, SGD and Deep Belief Network

Agenda
1. Case Study
2. Scraping reviews
3. Sentiment analysis
4. Classification using Doc2Vec (Logistic, SGD, SVM)
5. Challenges

Case Study
• Scrape the reviews of any website and perform sentiment analysis on the corpus
• Using sentiment analysis result, once the document is classified appropriately use it
to perform classification algorithm using doc2vec approach (SVM/ Deep Belief
Network)

Scraping Reviews
Programming Language used: R
Libraries required: Rvest, dplyr, tm, quanteda, etc
Approach:
1. From the main page of the product, navigate automatically to the review page
2. Loop through required number of pages to get the number of reviews required with an
average of 10 reviews per page
3. Reviews need to be saved in the disk directly to save read only memory and take
advantage of hard disk capacity
Note: Both the “page link” and the “number of pages” can be passed as an argument callable to
the script

Corpus Operations
Loading the documents using quanteda package
Quanteda will create Document Frequency Matrix by function dfm().
This function essentially does this by series of operation including tokenizing, lowercasing, indexing,
stemming, matching with dictionary
Hu and Liu’s lexicon
Using list of positive and negative words (dictionary) available from Hu and Liu’s lexicon with more than
6700+ words
All operations in one line!

Classification
Programming Language used: Python
Libraries required: gensim, nltk, sklearn, etc.
Approach:
1. Load the raw reviews and apply cleaning using nltk package (stop words, stemming,
numbers,etc)
2. Create TaggedDocuments required for building Doc2Vev models (both DM and DBOW)
3. Train both the model 10 times with random shuffling of the documents
4. Split the dataset and apply classification algorithms

DM and DBOW models
dbow (distributed bag of words)
It is a simpler model that ignores word order and
training stage is quicker. The model uses no-local
context/neighboring words in predictions
dm (distributed memory)
We treat the paragraph as an extra word. Then it is
concatenated/averaged with local context word vectors when
making predictions. During training, both paragraph and word
embeddings are updated. It calls for more computation and
complexity.

Checking the arrangements of Word vectors by both
models

4) Applying Classification Algorithm
Testing
Training

Deep Belief Network
Trained only on 3000 documents
Hyper parameters selected after 52 different combinations
In terms of learn_rates, decays, epochs and hidden units
Accuracy achieved of 89%

Conclusion
Deep Belief Network works well even with 3000 documents.
SVM performs poorly irrespective of the kernel and other hyper parameter
Best Model: Deep Belief Network

Challenges
1. Deep Belief Network does not work on Python 3
2. Need to setup Python 2.7.3 virtual environment
3. “nolearn” library compatibility issues
4. Python memory issues when working on large corpus. Does not work on CPU and needs a GPU
powered machine

Thank You
References:
https://districtdatalabs.silvrback.com/modern-methods-for-sentiment-analysis
https://www.zybuluo.com/HaomingJiang/note/462804

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Amazon Product Sentiment review

1. Amazon Product Review Sentiment Analysis Lalit Jain: https://www.linkedin.com/in/lalit7jain Big Data Systems & Analytics

2. Agenda 1. Case Study 2. Scraping reviews 3. Sentiment analysis 4. Classification using Doc2Vec (Logistic, SGD, SVM) 5. Challenges

3. Case Study • Scrape the reviews of any website and perform sentiment analysis on the corpus • Using sentiment analysis result, once the document is classified appropriately use it to perform classification algorithm using doc2vec approach (SVM/ Deep Belief Network)

4. Scraping Reviews Programming Language used: R Libraries required: Rvest, dplyr, tm, quanteda, etc Approach: 1. From the main page of the product, navigate automatically to the review page 2. Loop through required number of pages to get the number of reviews required with an average of 10 reviews per page 3. Reviews need to be saved in the disk directly to save read only memory and take advantage of hard disk capacity Note: Both the “page link” and the “number of pages” can be passed as an argument callable to the script

5. Results

6. Corpus Operations Loading the documents using quanteda package Quanteda will create Document Frequency Matrix by function dfm(). This function essentially does this by series of operation including tokenizing, lowercasing, indexing, stemming, matching with dictionary Hu and Liu’s lexicon Using list of positive and negative words (dictionary) available from Hu and Liu’s lexicon with more than 6700+ words All operations in one line!

7. Result

8. Visualizing the Sentiments

9. Classification Programming Language used: Python Libraries required: gensim, nltk, sklearn, etc. Approach: 1. Load the raw reviews and apply cleaning using nltk package (stop words, stemming, numbers,etc) 2. Create TaggedDocuments required for building Doc2Vev models (both DM and DBOW) 3. Train both the model 10 times with random shuffling of the documents 4. Split the dataset and apply classification algorithms

10. 1) Cleaning the loaded documents

11. 2) Create TaggedDocuments

12. DM and DBOW models dbow (distributed bag of words) It is a simpler model that ignores word order and training stage is quicker. The model uses no-local context/neighboring words in predictions dm (distributed memory) We treat the paragraph as an extra word. Then it is concatenated/averaged with local context word vectors when making predictions. During training, both paragraph and word embeddings are updated. It calls for more computation and complexity.

13. 3) Training the model

14. Checking the arrangements of Word vectors by both models

15. 4) Applying Classification Algorithm Testing Training

16. 4) Applying Classification Algorithm Testing Training

17. 4) Applying Classification Algorithm Testing Training

18. Deep Belief Network Trained only on 3000 documents Hyper parameters selected after 52 different combinations In terms of learn_rates, decays, epochs and hidden units Accuracy achieved of 89%

19. Conclusion Deep Belief Network works well even with 3000 documents. SVM performs poorly irrespective of the kernel and other hyper parameter Best Model: Deep Belief Network

20. Challenges 1. Deep Belief Network does not work on Python 3 2. Need to setup Python 2.7.3 virtual environment 3. “nolearn” library compatibility issues 4. Python memory issues when working on large corpus. Does not work on CPU and needs a GPU powered machine

21. Thank You References: https://districtdatalabs.silvrback.com/modern-methods-for-sentiment-analysis https://www.zybuluo.com/HaomingJiang/note/462804

Amazon Product Sentiment review

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