Sentiment Analysis of Emoji using Polarity in python

 Sentiment Analysis of Emoji using Polarity

 

 PROJECT ID: PYTHON24

 

PROJECT NAME: Sentiment Analysis of Emoji using Polarity

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

When starting a new NLP sentiment analysis project, it can be quite an overwhelming task to narrow down on a select methodology for a given application. Do we use a rule-based model, or do we train a model on our own data? Should we train a neural network, or will a simple linear model meet our requirements? Should we spend the time and effort in implementing our own text classification framework, or can we just use one off-the-shelf? How hard is it to interpret the results and understand why certain predictions were made?

This series aims at answering some of the above questions, with a focus on fine-grained sentiment analysis. Through the remaining sections, we’ll compare and discuss classification results using several well-known NLP libraries in Python. The methods described below fall under three broad categories:

Rule-based methods:

TextBlob: Simple rule-based API for sentiment analysis

VADER: Parsimonious rule-based model for sentiment analysis of social media text.

Feature-based methods:

Logistic Regression: Generalized linear model in Scikit-learn.

Support Vector Machine (SVM): Linear model in Scikit-learn with a stochastic gradient descent (SGD) optimizer for gradient loss.

Embedding-based methods:

FastText: An NLP library that uses highly efficient CPU-based representations of word embeddings for classification tasks.

Flair: A PyTorch-based framework for NLP tasks such as sequence tagging and classification.

 

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

What is the state-of-the-art?

The original RNTN implemented in the Stanford paper [Socher et al.] obtained an accuracy of 45.7% on the full-sentence sentiment classification. More recently, a Bi-attentive Classification Network (BCN) augmented with ELMo embeddings has been used to achieve a significantly higher accuracy of 54.7% on the SST-5 dataset. The current (as of 2019) state-of-the-art accuracy on the SST-5 dataset is 64.4%, by a method that uses sentence-level embeddings originally designed to solve a paraphrasing task — it ended up doing surprisingly well on fine-grained sentiment analysis as well.

Although neural language models have been getting increasingly powerful since 2018, it might take far bigger deep learning models (with far more parameters) augmented with knowledge-based methods (such as graphs) to achieve sufficient semantic context for accuracies of 70–80% in fine-grained sentiment analysis.

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

Other Information

 

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