Aspect Based Sentiment Analysis
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This document discusses aspect based sentiment analysis using recurrent neural networks. It describes annotating review data and developing a GUI for annotation. An API was created to extract aspect terms. A recurrent neural network model was implemented using Deeplearning4j with backpropagation through time to classify inputs. The system was trained on 1/3 of the data and achieved accuracy on the test data. Challenges included not reaching 100% accuracy and most terms being unrelated to aspects. Future work proposed using additional features and different neural network architectures.
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A. S., Shafie, Sharef, N. M., Murad, M. A. A., Azman, A., (2018), "Aspect Extraction Performance With Common Pattern of Dependency Relation in Multi Aspect Sentiment Analysis", 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP18), Kota Kinabalu, in press.Absa project
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This document contains a resume for Vaibhav Srivastav. It includes contact information, career objective, work experience at various companies from 2014 to present, technical skills in languages, databases, tools, servers and frameworks. It also lists projects undertaken including descriptions, technologies used, roles and responsibilities. Educational qualifications are provided including percentages obtained. Personal details like date of birth, sex, addresses and hobbies are mentioned.
Resume
Vivek Kumar Mishra is a senior systems engineer with over 3 years of experience working with Infosys and e-Infochips on various Oracle ERP and mobile application projects. He has extensive skills in Oracle technologies like PL/SQL, Oracle Forms, Reports, and Workflow as well as other tools like Shell Scripting. He is seeking a challenging position in software development that allows him to continue growing his technical skills and knowledge.
Stream SQL eventflow visual programming for real programmers presentation
Richard Tibbetts, CTO, StreamBase Systems.
StreamSQL EventFlow is one of the most popular languages for Complex Event Processing (CEP), a data management paradigm for real-time applications. Based on a stream-relational data model common to other CEP languages, EventFlow is unique in that it is a visual language. This talk will focus on the design of visual representations for key features including event dispatch, modularity, data parallelism, polymorphism, and dependency injection, and on the co-development of an Eclipse-based IDE along with a new programming language. StreamSQL EventFlow is the primary programming language for the StreamBase Event Processing Platform.
Complex Event Processing platforms are used to process large volumes of event-oriented data in real-time, often in latency-critical applications such as securities trading. Combining clustering, messaging, queuing, data storage, and application logic into one system minimizes latency and gives the programmer control over all aspects of the application.
StreamSQL EventFlow is an executable visual language for building CEP applications, unlike visual environments designed for non-developers, or architecture-focused modeling tools. The talk will cover experiences overcoming prejudice against visual programming languages, and how critical development tools are to that process. We will also discuss some details of the implementation including the compiler, a visual debugger, and diff/merge functionality.
IRJET- Voice to Code Editor using Speech Recognition
This document presents a summary of a research paper on developing a voice-controlled code editor using speech recognition. A team of students and a professor from S.B Jain Institute of Technology, Management and Research created a Java program editor that allows users to write code using voice commands. The editor takes advantage of the natural human ability to speak language and allows coding more accurately and intuitively compared to manual typing. It analyzes the user's speech using acoustic and language modeling with Hidden Markov Models to accurately recognize commands. The proposed voice-controlled code editor is designed to reduce typing errors, improve coding speed, and enable people with disabilities to operate a computer. It will support basic editing tasks and allow switching between voice and manual input.
Natural Language Processing - Research and Application Trends
This PPT is a Webinar delivered by Dr. Shreyas to explain the research and application trends in NLP area
DYNAMIC SLICING OF ASPECT-ORIENTED PROGRAMS
This document discusses dynamic slicing of aspect-oriented programs. It introduces aspect-oriented programming and the AspectJ language. It presents an algorithm for dynamic slicing of aspect-oriented programs that uses an execution trace file to construct a dynamic aspect-oriented dependence graph (DADG) and then performs graph traversal to compute the dynamic slice. The algorithm maps the dynamic slice back to the original program. The document also discusses applications of program slicing and provides average runtime results for dynamic slicing on programs of different sizes.
DYNAMIC SLICING OF ASPECT-ORIENTED PROGRAMS
This document discusses dynamic slicing of aspect-oriented programs. It introduces aspect-oriented programming and the AspectJ language. It presents an algorithm for dynamic slicing of aspect-oriented programs that uses an execution trace file to construct a dynamic aspect-oriented dependence graph (DADG) and then performs graph traversal to compute the dynamic slice. The algorithm maps the dynamic slice back to the original program. Experimental results show that the average runtime of the algorithm increases with the size of the program. Future work includes modifying the algorithm to avoid using a trace file and handling other program features.
Prasad Rompalli latest Resume
S V Prasad Rompalli is seeking a team leader or team member position utilizing over 11 years of experience in applications and production support using Java, Sybase databases, and Unix shell scripting. He has worked on several projects providing support for Java applications and databases, developing stored procedures, and writing Unix scripts. His objective is to work in a challenging position using his operational support skills while improving his knowledge and contributing to organizational growth.
Nirav Mehta
The document contains a resume for Nirav Jayesh Mehta. It summarizes his professional goal of joining a growing organization where he can develop skills across technologies. It details his 3 years and 7 months of experience as a Software Engineer at BNP Paribas ISPL, working on various projects involving Java, JSP, HTML, FileNet APIs, Struts and Hibernate. It lists his academic qualifications and awards received. It also provides personal details like languages spoken, date of birth and hobbies.
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Aspect Based Sentiment Analysis
- 1. Aspect Based Sentimental analysis Aspect Based Sentiment Analysis DR. Asif Ekbal Gaurav Kumar National Institute of Technology, Patna Mentor :- Summer Project
- 2. 12/10/2012 Your footer here
- 4. 12/04/2016 3 Our Objective Extract Aspect Term API to Extract Aspect Term
- 5. A Working Example 12/10/2012 Your footer here Sample InputVector Labels for Input Vector RNN Model Results
- 6. Example Continues 12/10/2012 Your footer here Its BIO term we get for some testing data Generated XML for Validation
- 7. Annotator for SEMIEVAL -14 12/10/2012 Your footer here
- 8. What’s New ? Use of RNN(Recurrent Neural Network) BPTT(Back Propagation Through Time ) LSTM (Long Short Term Memory) Stochastic Gradient Desecnt
- 9. Work Flow - 1 12/10/2012 Your footer here Raw Review Annotation Developed GUI for Annotation Generated XML Cohen’s Kappa AgreementResults 66 %
- 10. Work Flow - 2 12/10/2012 Your footer here Oh! Yes We got the data we need Algorithms Machine Learning Supervised Learning Supervised Unsupervised Reinforcement Classification Problem Regression Problem This is what our problem requires
- 11. Final Work Flow 12/10/2012 Your footer here • 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 API To Extract Aspect Term
- 12. Deep Learning4 Java Working 12/10/2012 Your footer here • 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
- 13. Sample Code This is how we I am configuring RNN Fitting the Three Dimension Input Vector Training Starts Here
- 14. Results of Aspect Based Analysis Trained on 1/3 data got the accuracy for Testing data This accuracy is for Training data
- 15. 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.
- 16. 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.
- 17. 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 !!!!!
- 18. Any Questions?