Challenges of event detection from social media streams

Abstract

The area of Event Detection (ED) has attracted researchers' attention over the last few years because of the wide use of social media. Many studies have examined the problem of ED in various social media platforms, like Twitter, Facebook, YouTube, etc. The ED task for social networks involves many issues, including the processing of huge volumes of data with a high level of noise, data collection and privacy issues, etc. Hence, this article discusses and presents the wide range of challenges encountered in the ED process from unstructured text data for the most popular Social Networks (SNs), such as Facebook and Twitter. The main goal is to aid the researchers to understand the main challenges and to discuss the future directions in the ED area.

KNN classifier based approach for multi-class sentiment analysis of twitter data

“…A significant proportion of customers and clients who utilize a product or service generate a huge amount of data in the form of comments, feedbacks and reviews that express their opinions. However, developing applications for analyzing such data involves several challenges, considering the enormous size of such data available and the structure of the data [8], [9]. The presence of informal words, slang words, and abbreviations make it difficult to identify and classify sentiments.…”