Enabling Semantic User Context to Enhance Twitter Location Prediction in python

 

Enabling Semantic User Context to Enhance Twitter Location Prediction

 

 PROJECT ID: PYTHON11

 

PROJECT NAME: E Enabling Semantic User Context to Enhance Twitter Location Prediction

 

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

 

PROJECT ABSTRACT:

Knowledge bases have been used to improve performance in applications ranging from web search and event detection to entity recognition and disambiguation. More recently, knowledge bases have been used to analyze social data. A key challenge in social data analysis has been the identification of the geographic location of online users in a social network such as Twitter. Existing approaches to predict the location of users, based on their tweets, rely solely on social media features or probabilistic language models. These approaches are supervised and require large training dataset of geo-tagged tweets to build their models. As most Twitter users are reluctant to publish their location, the collection of geo-tagged tweets is a time intensive process. To address this issue, we present an alternative, knowledge-based approach to predict a Twitter user’s location at the city level. Our approach utilizes Wikipedia as a source of knowledge base by exploiting its hyperlink structure. Our experiments, on a publicly available dataset demonstrate comparable performance to the state of the art techniques.

Location of Twitter users is a prominent attribute for many applications such as emergency management and disaster response [15], trend prediction [1], and event detection [24]. Twitter users can choose to publish their location information by way of (1) geo-tagging their tweets, or (2) specifying it in the location field of their Twitter profile. However, recent studies have shown that less than 4 % of tweets are geo-tagged [13, 19]. Also, while many users choose to leave the location field of their profile empty or enter invalid information, others specify location at different granularity such as city, state, and country. Thus, most of the information entered in this field cannot be reverse geocoded to a city. For instance, Cheng et al. [5] found that, in their dataset comprising of 1 million Twitter users, only 26 % of the users shared their location at the city level.

 

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

 

Conclusion:

 we presented a novel knowledge based approach that uses Wikipedia to predict the location of Twitter users. We introduced the concept of local entities for each city and demonstrated the results of different measures to compute the localness of the entities with respect to a city. Without any training dataset, our approach performs comparable to the state of the art content based approaches. Furthermore, our approach can expand the knowledge base to include other cities which is remarkably less laborious than creating and modeling a training dataset.

 

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          

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