aspect based sentiment analysis

Aspect Based Sentiment Analysis

Aspect Based Sentimental analysis

DR. Asif Ekbal

Gaurav Kumar National Institute of Technology, Patna

Mentor :-

Summer Project

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Special ThanksTO

Shad Sir(Mentor)

2

Aspect Based Sentiments Analysis?

12/04/2016

312/04/2016

Our Objective

Stock Prediction

Flight Ranking

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A Working Example

12/10/2012

Sample InputVector

Labels for Input Vector

RNN Model

Results

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Example Continues

12/10/2012

Its BIO term we get for some testing data

Generated XML for Validation

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Annotator for SEMIEVAL -14

12/10/2012

What’s New ?

Use of RNN(Recurrent Neural Network)

BPTT(Back Propagation Through Time )

LSTM (Long Short Term Memory) Stochastic Gradient Desecnt

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Work Flow - 1

12/10/2012

Raw Review Annotation

Developed GUI for Annotation

Generated XML

Cohen’s Kappa AgreementResults

66 %

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Work Flow - 2

12/10/2012

Oh! Yes We got the data we

need Algorithms

Machine Learning

Supervised Learning

Supervised

Unsupervised

Reinforcement

ClassificationProblem

RegressionProblem

This is what our problem requires

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Final Work Flow

12/10/2012

• Classification Problem with fixed output Labels.

Annotated Text

Mathematical Representation

Word2Vector

Deep Learning

Recurrent Neural Network using DL4j

Results---Accuracy

BPTT

A bunch of Program t o bring data in format

Developed APITo Extract Aspect

Term

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Deep Learning4 Java Working

12/10/2012

• It is java Library to configure all types of Deep Learning Nets.• It has built-in GPU support.

» MAP-REDUCE PROCESS

» These Steps are Repeated untill we get minimum error.

INPUT DATA

1 2 3 4 NN-1

Weighted and Bias are Averaged

1 2 3 4 NN -1

Step 1

Clusters

Step 2

Weights are

Updated

Sample Code This is how we I am configuring

RNN

Fitting the Three

Dimension Input Vector

Training Starts Here

Results of Aspect Based Analysis

Trained on 1/3 data got the accuracy for Testing data

This accuracy is for Training data

Challenges to our approach

• Accuracy: Not 100 per cent

• A lot of Others terms: It means for extracting B-(beginning of Aspect term), I-(Intermediate Aspect Term), O(others). There is lot others terms. Even less than 1 percent belongs to B,I category and all 99 percent comes in O category.

• Need for lot of data: It seems like we required some more features from data to be more accurate.

Work Done During Internship Period

◎ Read the famous researchers and there Work.◎ Completed the Annotation of SEMIEVAL 14 about 5417 annotations.◎ Made GUI for Annotation of SEMIEVAL 14.◎ Completed the Annotation of SEMIEVAL 15-16 about 5417 annotations.◎ Program to Find N-grams ◎ Read some basic concepts of Deep Learning and its applications.◎ Implemented Recurrent Neural Network In Deep Learning4j.◎ Implemented code to Extract Aspect Term in RNN in Java.◎ Developed a API and a GUI for exacting Aspect Term.◎ Some other Basic Programs like XML generation,Word2Vector and many more.

◎ Self Implemented Works Are: -◎ Implemented Back propagation from Scratch in Java.◎ Implemented KNN, AutoEncoder etc in java.◎ Implemented to configure Neural Network using Feed Forward in Java from Scratch.◎ Implemented Linear Regression in Java from scratch.

Future Scope or Enhancement◎ We can provide more no of features in our data such as the

position of BIO terms as a feature.

◎ We can use Recursive Tensor Neural Network which works well for sentiments analysis and it is shown by Meta-Mind they have developed App on Twitter Sentiments Analysis.

◎ We need to extract some Outliers in our data.

◎ We can use Bootstrap Re-sampling of our Data.

◎ Any Suggestions Please Give !!!!!

Any Questions?