Short Communication Open Access

Quality assessment of user generated content on twitter-A deep learning based approach

Abstract

Social Media today may be a platform for many active users globally to share their content. Each second, there are thousands of messages or comments posted on different social networks. With these staggering numbers of user generated content (UGC), challenges are sure to surface. One such challenge is to assess the standard of UGC in social media because the content generated in social media could have positive or negative impact on fellow users and customary people too. inferiority content not only impacts the user’s content browsing experience, but also deteriorates the aesthetic value of social media. Therefore, our aim is to assess the standard of content accurately to market the propagation of top quality content. Successful assessment of quality of UGC in social media fosters the expansion of high utility UGC, which might be employed by other applications and organizations for societal or organizational benefits. during this paper, we propose a deep learning based model, that leverages the standard assessment of UGC. The experimental results demonstrate that our proposed model leads to high accuracy and low loss. Social media platforms, forums, blogs, and opinion sites generate vast amount of knowledge . Such data within the sort of opinions, emotions, and views about services, politics, and products are characterized by unstructured format. End users, business industries, and politicians are highly influenced by sentiments of the people expressed on social media platforms. Therefore, extracting, analyzing, summarizing, and predicting the emotions from large unstructured data needs automated sentiment analysis. Sentiment analysis is an automatic process of extracting the opinionated from data and classifying the emotions as positive, negative, and neutral. Lack of enough labeled data for sentiment analysis is one among the crucial challenges in tongue processing. Deep learning has emanated together of the highly sought-after solutions to deal with this challenge thanks to automated and hierarchical learning capability inherently supported by deep learning models. Considering the appliance of deep learning approaches for sentiment analysis, this chapter aims to place forth taxonomy of traits to be considered for deep learning-based sentiment analysis and demystify the role of deep learning approaches for sentiment analysis. Sentiment analysis is that the automated process of analyzing text data and sorting it into sentiments positive, negative, or neutral. Using sentiment analysis tools to research opinions in Twitter data can help companies understand how people are talking about their brand.With quite 321 million active users, sending a daily average of 500 million Tweets, Twitter allows businesses to succeed in a broad audience and connect with customers without intermediaries. On the downside, it’s harder for brands to quickly detect negative content, and if it goes viral you would possibly find yourself with an unexpected PR crisis on your hands.This is one among the explanations why social listening ― monitoring conversations on social media platforms ― has become an important process in social media marketing. Monitoring Twitter allows companies to know their audience, keep it up top of what’s being said about their brand and their competitors, and find out new trends within the industry.

Irfan Mohiuddin

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