Using short URLs in Twitter messages has increased in popularity in the past few years. This is mostly due to the fact that Twitter, as one of the most popular social media networks, imposes a 140 character limit to the messages distributed over the network. This paper analyzes the use of short URLs by Twitter users. Specifically, the goal is to examine the content pointed by the short URLs as well as the potential impact on the performance of sentiment analysis (opinion mining) tasks. Opinion mining based on Twitter feed has been used in an array of applications, including healthcare, identifying public opinion on political issues, financial modeling and advertising. Past research has however completely disregarded tweets which contain URLs. It is not hard to see how opinion mining can be improved considering the fact that Twitter users regularly post URLs pointing to articles endorsing a particular political figure, articles in important financial outlets or reviews of products. This study is based on the analysis of three distinct Twitter datasets with varying number of tweets which include short URLs. Popular machine learning techniques used in opinion mining were deployed in different experimental settings to conclude which are the most lucrative options. © 2017 IEEE.
|Title of host publication||Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017|
|Publication status||Published - Dec 2017|
|Event||16th IEEE International Conference on Machine Learning and Applications (ICMLA) - Mexico, Cancun, Mexico|
Duration: 18 Dec 2017 → 21 Dec 2017
|Conference||16th IEEE International Conference on Machine Learning and Applications (ICMLA)|
|Period||18/12/17 → 21/12/17|