What language is explored in this paper?
English language
Twitter is a social media platform where users express opinions over a variety of issues. Posts offering grievances or complaints can be utilized by private/ public organizations to improve their service and promptly gauge a low-cost assessment. In this paper, we propose an iterative methodology which aims to identify complaint based posts pertaining to the transport domain. We perform comprehensive evaluations along with releasing a novel dataset for the research purposes.
With the advent of social media platforms, increasing user base address their grievances over these platforms, in the form of complaints. According to BIBREF0, complaint is considered to be a basic speech act used to express negative mismatch between the expectation and reality. Transportation and its related logistics industries are the backbones of every economy. Many transport organizations rely on complaints gathered via these platforms to improve their services, hence understanding these are important for: (1) linguists to identify human expressions of criticism and (2) organizations to improve their query response time and address concerns effectively. Presence of inevitable noise, sparse content along with rephrased and structurally morphed instances of posts, make the task at hand difficult BIBREF1. Previous works BIBREF2 in the domain of complaint extraction have focused on static datasets only. These are not robust to changes in the trends reflected, information flow and linguistic variations. We propose an iterative, semi-supervised approach for identification of complaint based tweets, having the ability to be replicated for stream of information flow. The preference of a semi-supervised approach over supervised ones is due to the stated reasons: (a) the task of isolating the training set, make supervised tasks less attractive and impractical and (b) imbalance between the subjective and objective classes lead to poor performance.