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Hello! I am Aftab Falak, I am a full-stack web developer. I have more than 4 years of experience in web development. I have experienced in PHP, MySql, Laravel, WordPress, REACT. js, Node. js, MongoDB, Html5, CSS3, Javascript, JQuery, Ajax, Profile ©

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My studies in Mathematics, Computer Engineering and Computer Science have provided me a strong theoretical background and research experience and also has created several opportunities for me to use my knowledge in practice. I have gained invaluable experience through founding and working in the West Bakhtyar System Company from 2008 where I supervised and participated in many projects as Manager and Software Developer. Also, the Developer role in West Ava Pardaz has helped me to improve my research, managing and teamwork abilities. Some of the notable works that I carried out during my vocational experiences were the development of Online eCommerce and Accountancy Website, Windows Accountancy Software, Infirmary Management Systems, School Management System, etc. I have included a short list of my accomplished projects in my CV. Also, for a brighter future I achieved to become an expert in some programming languages such as C++/C, C#, Java, VB, Python, SQL, etc. Additionally, I am completely familiar with parallel programming and I have experience of developing parallel software. Furthermore, I equipped myself with professional software such as MATLAB. In my master thesis, I am currently working on sentiment analysis for product aspect extraction. Our work in this project involves extracting and analyzing aspects for different products existing in large datasets, currently Amazon and Twitter for English, and SentiPers for Persian. Using deep learning and specifically Recursive Deep Models and CRF (Conditional Random Fields) we have developed a model to extract and determine aspects and their sentimental phrases, intensities and polarities and have mined the opinions by extracting quintuples from the opinions consisting of (entity, aspect, orientation, holder, time). Based on these information, interesting, relevant and highly useful information can be acquired about the data which can lead to creation of extensive and powerful recommendation/evaluation systems. These systems in turn, can direct the resources in hand, for both producers and consumers in decision making. We use TensorFlow by Google as the primary tool for our experiments and Python is extensively used for writing the scripts and communicating with TensorFlow. Our work already has revealed interesting information and an extension of our work will be prepared for publication in a relevant journal in the near future. ©

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