Contextually Request Tweets Classification Based on Deep Features

Abstract

Millions of tweets are posted on Twitter related to different events and situations. As a result, many research problems have been identified for Twitter data. One of them is tweet classification, especially classification of tweets posted during critical situations, disasters, or in the time of need. Request classification is a sensitive issue as it helps to save precious lives. In previously proposed methodologies, a lot of feature extraction techniques have been used for tweet classification. These techniques lack a lot of aspects that should be considered for request classification. Features extracted by those techniques do not contain any contextual information of the text. Moreover, features are limited to word level. Another issue is the curse of dimensionality due to n-gram features. Metadata is also used as features in some of the proposed methodologies and it did not contribute to the performance of the framework. Improvement in feature extraction methods can lead to better results. This work aims to develop a deep learning-based framework which extracts contextual features from tweets. These contextual features are more reliable, avoid the curse of dimensionality and capture semantic information. Moreover, sentence-level feature has also been extracted from tweets. Three different feature sets have been extracted to achieve maximum output. Multiple classifiers have been used to validate the performance of this framework. In the end, 612-dimensional hybrid features have been created and experimented using four different classifiers. Neural Networks outperformed other classifiers and produced improved results as compared to the baseline model. The proposed framework achieved 89% accuracy, 96% precision, 90% recall, and 93% F1- score using hybrid features with Hold-out validation.