Table of Contents
An initial presentation
A study was conducted to analyze the results. The research concluded that the results were consistent.
Scope of Problem
This is an introduction.
Globalized media has changed the way people consume media. News creation, dissemination, and usage are not the same as in pre-innovation, when people relied only on 24-hour news media. To keep up with the latest news, people are increasingly going online. The media landscape is changing rapidly with the advent of social media in recent news creation and distribution.
Globalization is affecting all aspects and disrupting data innovation. This has profoundly altered the news generation process and dispersion. Nowadays, everyone can easily access the internet and engage with news on a global scale. Wikipedia defines social media as an online innovation that allows users to share information and thoughts through virtual networks. Facebook, LinkedIn, Twitter, and Twitter all allow users to communicate with each other via status updates, photo, video, or text. It is clear that the media standard and journalism conventions are changing because social media allows users to update their statuses, post pictures and videos. Text mining is a method of extracting indefinite and useful models from large amounts of unstructured data. Scholars have stated that text mining is incorporating data mining into computational linguistics and information retrieval (IR), as well as data mining. To improve the accuracy of reading text, many text mining techniques were developed such as topic detection and track, keyword extractions, sentiment analysis, document groupings, automatic document summarization and keyword extraction. NPL, a correlated field of text mining, raises concerns about the interrelationships between unstructured texts. Recent media discussions have highlighted the potential for research in news generation and user generated content.
MotivationBangla is now one of most commonly spoken languages. Daily, many Bangla posts get shared by Facebook pages from different Bangladeshi newspapers. Texts in Bangla are not structured and must be converted into information from large amounts of data by using text mining techniques. There is not much literature available on Bangla texts, and more specifically on Bangla news. This study explores the methods of analyzing news articles textual Bangla on social media. My research was motivated by the vast availability of Bangla texts, which I hope to convert into useful knowledge.
Background Background is essential for the success of any research. The background study is necessary to help you design your thesis. This section covers all existing relevant works as well the challenges and scopes.
Numerous scholars revealed that social media is increasingly attracting writers and readers. Social media’s influence is based on its quality and reputation among internet users worldwide. Mass media has become obsolete due to social media like Facebook. Today, personal media dominates. Facebook is a social network that provides users with access to news pages from newspapers. They can choose to read what they want or not. Online users basically choose the news and views that matter to them most.
An investigation looked at different text mining techniques to analyze social network textual models and online-based applications. The survey found that authors sought to give a vast knowledge of the different text mining methods and their uses in social networking sites. The two most important approaches to text mining for intellectual unstructured content analysis include clustering or classification.
Recent text mining and analytics research was carried out. The study examined the unstructured English content of various Facebook posts. This study demonstrated several methods for analyzing unstructured raw data and their transformation into quantifiable information. RapidMiner, a data science environment, was used to analyze the Facebook data.
Facebook wanted to gather information about the sentiments of its users, and it paid particular attention to the “Arab Spring”, which was a crucial period in history. A system was created using Support Vector Machines (SVM) as well as Naive Bayes. A lexical resource to analyze sentiment is created from interjections, emoticons, and acronyms that are derived form the status updates. The research revealed some profound insights about Tunisian Facebook users’ January 2011 Tunisian revolution. However, there were a few flaws in the analysis that focused on users’ emotions changing at a particular point. The exchange and time dependence factors were not considered in the examination, which affected the findings. The study would have been even more interesting if it had included this time-related element in its investigation.
We did some research and discovered that customers can post a lot of valuable information every day. This information could be very beneficial to organizations. The use of social media sites is increasing slowly but surely. This case study will show you how analysis of data from social media can have a significant impact on decision makers as well as management research and practice. Data was collected via the SAMSUNG mobile facebook page. With ‘NCapture for NVivo 10,’ 128,371 users submitted comments that were used to capture the research corpus from 10th June to 11th September.
Researchers recommend the structured approach to analyze data from social media that only contains comments in English. Researchers suggested a straightforward way to access existing knowledge in order for social media data to be quantifiable. You can quantify this in surveys, studies, and plan-of-decision-making frameworks. The research did not find any evidence of a changing example or progression for Facebook users.
Students can also use social media platforms to communicate their views and feelings. This area has been a popular field of research for many researchers. Researchers considered student conversations over web-based media that focused on emotions, opinions, concerns and learning. The researcher examined the tweets of 25.000 students in engineering about school life. The research revealed that many issues such as sleep problems, study load, and insufficient social engagement were the result of this investigation.
A further investigation focused on the extraction of knowledge from information on social media sites that was available to university students. K-means is a data mining technique that extracts constructive information from the educational sector. The author created a questionnaire for students in different fields of study and then used data mining to analyze the results. Study revealed that university students most frequently use Twitter, Orkut, Facebook.
Research SummaryBoth novice and experienced researchers have found learning technologies, analytics and text mining to be extremely useful. These issues, as well the limitations of laboring to analyze qualitative data and user generated textual contents, have been solved.
Scope and nature of the problem. Although significant research has been conducted on social media data extraction, it seems that newspapers’ social networking data analyses are often overlooked. Unstructured Bangla News Analysis has yet to be studied. This study is based on the analysis of large-scale data sets gathered from three Facebook pages of popular newspapers.
The challenges Despite the fact that there is a lot of data available online, it was difficult to extract a large amount of data.
I found it difficult to reconcile Bangla text with the current system. This was the biggest challenge that I faced during my research. Bangla Language also has more stop words and uses different punctuations. Additionally, the preprocessing phase was more difficult because extracted data from Facebook had many non-essential variables. This required that they be eliminated in order for data sets to be efficient.