Opinion Mining and thought Pattern Classification with Natural Language Processing (NLP) Tools

Opinion mining from digital media is becoming the easiest way to obtain trivial aspects of the thinking trends. Currently, there exists no hard and fast modeling or classification over this for any society or global community. The marketing companies are currently relying on sentiment analysis for their products. In this paper social sentiment is focused on the form of collective sentiment and individual sentiment; we intend to classify these in the form of Macro and Micro-social sentiment. The sentiment varies among groups, sects etc. and various classes of society are depending on many other characteristics of the society. The social media is available to explore certain ideas, various trends, and their significance. The significance requires further exploration of more patterns and this cycle continues. The exploration cycle focuses on a research outcome. Based on above all the study focuses on the opinion classes towards the general think patterns. The Think Patterns (TP) are developed over time due to social traditions, fashions, family norms etc. The specific community think patterns are very difficult to classify like a female in restricted societies or rural societies of our country. Such trends and patterns are the focus of this study based on various defined parameters. The opinion and sentiment data analysis will be assessed using natural language processing (NLP) tools, Twitter, GATE, Google API’s, etc.

@article{Naqvi2018,
  title     = {Opinion Mining and thought Pattern Classification with Natural Language Processing (NLP) Tools},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {10},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Sayyada Muntaha Azim Naqvi and Muhammad Awais and Muhammad Yahya Saeed and Muhammad Mohsin Ashraf},
  doi       = {10.14569/IJACSA.2018.091059},
  url       = {https://doi.org/10.14569/IJACSA.2018.091059}
}