Classifying Short Text in Social Media: Twitter as Case Study

Abstract

With the huge growth of social media, especially with 500 million Twitter messages being posted per day, analyzing these messages has caught intense interest of researchers. Topics of interest include micro-blog summarization, breaking news detection, opinion mining and discovering trending topics. In information extraction, researchers face challenges in applying data mining techniques due to the short length of tweets as opposed to normal text with longer length documents. Short messages lead to less accurate results. This has motivated investigation of efficient algorithms to overcome problems that arise due to the short and often informal text of tweets. Another challenge that researchers face is stream data, which refers to the huge and dynamic flow of text generated continuously from social media. In this paper, we discuss the possibility of implementing successful solutions that can be used to overcome the inconclusiveness of short texts. In addition, we discuss methods that overcome stream data problems.

Analyzing Tweets on New Norm: Work from Home during COVID-19 Outbreak

“…In our preliminary analysis of a sample of tweets, we observed that the tweets dataset contains both advertisements and nonadvertisements content. As advertisements are irrelevant to our project's objective, we filter them first before analyzing the content using the classification approach [4].…”

Implementation of a Journalist Business Intelligence in Social Media Monitoring System

“…There are several methods that could be implemented in reducing that by selecting the appropriate method. One of them would be a support vector machine or known as SVM (Support Vector Machine) [15]. In terms of NLP, SVM is one of the machine learning models that is often used.…”

Regularized Graph Convolutional Networks for Short Text Classification

“…Short-text classification is a common problem in information retrieval (Ji et al, 2014) and has applications in several domains including e-commerce (Yu et al, 2012;Shen et al, 2009), social media (Kateb and Kalita, 2015), healthcare (Pestian et al, 2007) and cognitive-biometric recognition (Pokhriyal et al, 2016). In this paper, we develop a short text classification technique for solving two problems relevant to product search on e-commerce platform: 1) Product Query Classification (PQC) -When the customer enters a free form query, it is important to understand their product type intent to recommend and advertise the relevant products.…”