1.1 Introduction
The Filtering Political Sentiment in Social Media from Textual Information system is capable of filtering the political hazardous sentiments using text mining and web mining techniques. To understand sentiment of any social media post this method can be used. This can be used to create peaceful society by reducing political turbulence. The classification depends on dictionary that we provided and training to the words. Due to the rapid growth of the Internet, the networked population has been increasing to a few billions (Elif et al., 2014). As a result, social media plays a vital role in the life of civil society worldwide and people are gaining greater access to information, more opportunities to engage themselves in handling public opinions and an enhanced ability to undertake collective action. As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Limitations of the Study and Definition of technical terms.
1.2 Background of Study
The early days of social networking sites were viewed merely for friendship or dating purposes. But, with the progress of time the characteristics of most of the social media are now changing. For example, in 2008 and 2012 US presidential elections, social media were used for election campaign and for the engagement of the youth (Kristin, 2011). Again in 2009, social media - twitter was used efficiently by the politically interested people and the politicians during German general election to conduct the election campaign and to discuss political events (Pascal, 2011). Nowadays social media is playing a important role in affecting people’s feelings in side or opposite a government or an institution. Therefore, to understand the sentiment of any posting in social media, an effective procedure is necessity. Some Facebook postings were analyzed to understand political sentiments. In any politically infected posting, there are some dominant words.
At beginning, we have created a dictionary consisting of unique words gathered from political or nonpolitical posts or comments and then trained using Naïve Bayes algorithm based on probability algorithm. To identify the sentiment expressed in a new post or comment, we have taken each word of the posting and then compared those with the dictionary words for classification. At last, we have tested our algorithm using two hundred postings from Facebook and our result shows that the method can classify posts or comments with good accuracy.
A lot of political gurus, academics and journalists have embraced social media as an undeniable force for good, claiming that democracy is just a tweet away. Moreover, political specialists believe that if people want to liberate a society, they need to use Internet in a proper way. Internet has been heralded as an effective weapon of the weak people against their authoritarian leaders. It is now an ongoing phenomenon that on one side government thugs firing bullet and on the other side.
1.3 Statement of Problems
Investigation revealed the problems of the Filtering Political Sentiment in Social Media from Textual Information existing system which entails the malicious political sentiments or comments from Facebook post which plays a important role in influencing the people’s and societies sentiments, the researcher proposed the filtering the political sentiments of by using textual information. The Researcher will have provided dictionary which will contain words that may hurt someone’s sentiment.
1.4 Aim and Objectives of Study
The aim of the study is to design and implement a system that will Filter Political Sentiment in Social Media from Textual Information. In achieving this aim, the following specific objectives were laid out as follows:
- To implement a program where a social media post or comment written in English language as an input to classify its sentiment either political or nonpolitical.
- To detect and prevent the malicious political sentiments or comments from Facebook post which plays a important role in influencing the people’s and societies sentiments.
- To understand sentiment of any social media post this method can be used.
- To reduce political turbulence and stop prank in social Media.
- To classify posts or comments with good accuracy and maintain peaceful environment in social media.
1.5 Significance of Study
The following are the significance of this study:
- The results of this study will throw light on approaches to Filtering Political Sentiment in Social Media from Textual Information and how it can be used as a tool for national development.
- This research will also serve as a resource base to other scholars and researchers interested in carrying out further research in this field subsequently, if applied will go to an extent to provide new explanation to the topic.
1.6 Scope of Study
The study focuses on the Filtering Political Sentiment in Social Media from Textual Information using Facebook social media platform as a case study.
1.7 Limitations of the Study
During the course of this study, many things militated against its completion, some of which are:
- Time Constraint: The time frame given to accomplish this project was very short due to school academic calendar and it was carried out under pressure which made the researcher not to implement some necessary features.
- Research material: availability of research material is a major setback to the scope of the study.
- Frequent power failure: This made the researcher append more money on fuel to ensure sustainable power.
- Financial Constraint: Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet).
1.8 Definition of Terms
Learning Objects: In the context of this research, a learning-object-is-a-resource with a clear educational-application. It is in digital form − examples: a Microsoft Word Document or a PDF document.
Catalog: This research applies the word ‘catalog’ as a container that presents all the SICT projects logged/stored in an organized tabular format, showing relevant information about each project. It can be compared to a library catalog containing all the information about books − their titles, author, number of pages, published year, ISBN etc.
Repository: A repository (in this case; SICT repository) is an archive, storehouse or container that allows storage, cataloging, accessing all SICT undergraduate projects and thesis, and viewing each object’s content.
Identifier: An identifier is simply a unique identity attached to one object, it aids referencing such object directly when its identifier is requested. An identifier can be a serial number, identity number (also written as ‘ID’) or an index number.
Object: An object in the context of this research refers to a project record returned by a database − queried using an Object Oriented Programming (OOP) approach. An object (i.e. a project record) returns or contains the project topic, student name, abstract, year of project submission and the date and time the object record was created.
Database: A database is a repository that allows storage, retrieval and manipulation of data. A database can be used efficiently with the aid of a Database Management System (DBMS) − a set of tools that allow storage, access, retrieval and maintenance of data stored in a database, examples of DBMS include XAMPP, WAMP, LAMP and AppServ to mention a few.
Web-Browser: A web-browser, internet browser or browser is software program that interprets the codes written in markup languages in graphic and visual (like images, text, audio or animation) form. This allows users to easily request and access a website or to search for information through a search engine (either Google, Ask, Bing and AOL among others).
Keyword: A phrase or just one word that is used to search for a certain result or set of results.
User: A user is system, application, request or person that can use a computer or software to perform a specific task.
Metadata: A set of data that describes and gives information about other data.
Dataset: A collection of related game plans of information that is made out of disengaged segments however can be controlled as a unit by a PC.
Relational Database: A database composed to see association among relations of information delineates tables from which data can be gotten to or reassembled in different courses without rearranging the database tables.
