Sentiment Analysis of Twitter data using four machine learning algorithm in python

 

Sentiment Analysis of Twitter data using four machine leaarning algorithm

 

 PROJECT ID: PYTHON25

 

PROJECT NAME: Sentiment Analysis of Twitter data using four machine learning algorithm

 

 

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

 

PROJECT ABSTRACT:

Sentiment analysis is the automated process of analyzing text data and sorting it into sentiments positive, negative, or neutral. Using sentiment analysis tools to analyze opinions in Twitter data can help companies understand how people are talking about their brand.

 

Twitter boasts 330 million monthly active users, which allows businesses to reach a broad audience and connect with customers without intermediaries. On the downside, there’s so much information that it’s hard for brands to quickly detect negative social mentions that could harm their business.

 

This is why social listening, which involves monitoring conversations on social media platforms, has become a key strategy in social media marketing.

 

Listening to customers on Twitter allows companies to understand their audience, keep on top of what’s being said about their brand, and their competitors, and discover new trends in the industry.

 

In this guide, you’ll learn more about how to analyze sentiments in Twitter data, using code-free sentiment analysis tools and sentiment analysis in Python.

 

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

 

Setting up Software Environment

Python is a high level, interpreted, interactive and object-oriented scripting language.

Python is designed to be highly readable and has fewer syntactical constructions than other languages. Python is used in the development of this model. In this experiment, the following python libraries are used to develop the machine learning models:

• NLTK: It is a python package which works with human language data and provides an easy-to-use interface to different lexical resources like WordNet and text processing libraries. These lexical resources are used for classification, tokenization, stemming, tagging, parsing, and semantic reasoning [23].

• Pandas: It is a python package which acts as a data analysis tool and deals with data structures. Pandas carry out entire data analysis workflow in Python without having to switch to a more domain specific language like R [46].

• Tweepy: It is used in accessing the Twitter API by establishing the connection and to gather tweets from Twitter [24]. This module is used to stream live tweets directly from Twitter in real-time.

• Numpy: NumPy is the fundamental package for computing with Python. It is used to add support to multi-dimensional arrays and matrices, with a large collection of high-level mathematical functions [47].

• scikit-learn: It is a simple and efficient tool for data mining and data analysis [47]. • matplotlib python library which generates plots, histograms, power spectra, bar charts, etc.

In this work matplotlib.pyplot module is used to plot the metrics [47].

• Gensim It is used to automatically extract semantic topics from documents, as efficiently as possible. Gensim is designed to process raw, unstructured text data. The algorithms in Gensim, such as Word2Vec where it automatically discovers the semantic structure of phrase by examining statistical co-occurrence patterns within a corpus of training documents. These algorithms are unsupervised. Once these statistical patterns are found, any plain text documents can be succinctly expressed in the new, semantic representation and queried for topical similarity against other documents [48].

• Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research [49]

 

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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