Analysis of Damage Assessment Tweets During Disaster using Sentiment Analysis

This seems to be an abstract or summary of a paper on monitoring Twitter for damage assessments after a disaster. Using simple linear regression and Support Vector Regression methods for weighting and the random forest methodology for classification, the research provides a novel approach that makes use of low-level lexical characteristics, top-most frequency word features, and syntactic elements relevant to damage assessment. The accuracy of the suggested method for identifying damage assessment tweets is 94.62%, as measured across 14 typical disaster datasets for binary and multi-class categorization. Significant advancements were observed when comparing the proposed method to the state-of-the-art for both in-domain and cross-domain scenarios. The suggested method does not require labelled tweets or tweets of a specific disaster kind in order to be trained and implemented; instead, it can be trained on historical disaster datasets.