2018/10/26 by Bhaskar Gautam, Gautam, Bhaskar, B. Annappa +2
Computer Science · Social Sciences · #Disaster Management and Resilience #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Public Relations and Crisis Communication #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI) #cs.IR #cs.SI
paper · pdf · doi:10.48550/arxiv.1810.11498
Working notes of IRMiDis Track {FIRE} 2017 - Forum for Information Retrieval Evaluation, Bangalore, India, December 8-10, 2017, http://ceur-ws.org/Vol-2036
arxiv created 2018/10/26 · openalex publication_date 2018/10/26 · arxiv updated 2018/10/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Online social microblogging platforms including Twitter are increasingly used for aiding relief operations during disaster events. During most of the calamities that can be natural disasters or even armed attacks, non-governmental organizations look for critical information about resources to support effected people. Despite the recent advancement of natural language processing with deep neural networks, retrieval and ranking of short text becomes a challenging task because a lot of conversational and sympathy content merged with the critical information. In this paper, we address the problem of categorical information retrieval and ranking of most relevance information while considering the presence of short-text and multilingual languages that arise during such events. Our proposed model is based on the formation of embedding vector with the help of textual and statistical preprocessing, and finally, entire training 2,100,000 vectors were normalized using feed-forward neural network for need and availability tweets. Another important contribution of this paper lies in novel weighted Ranking Key algorithm based on top five general terms to rank the classified tweets in most relevance with classification. Lastly, we test our model on Nepal Earthquake dataset (contains short text and multilingual language tweets) and achieved 6.81% of mean average precision on 5,250,000 unlabeled embedding vectors of disaster relief tweets.