2017/07/19 by Prannay Khosla, Khosla, Prannay, Moumita Basu +5 · 1 citation
Computer Science · #FOS: Computer and information sciences #H.3.3 #Information Retrieval (cs.IR) #Sentiment Analysis and Opinion Mining #Text and Document Classification Technologies #Topic Modeling #cs.IR
paper · pdf · doi:10.48550/arxiv.1707.06112
8 pages, 7 figures; SIGIR 2017 Workshop on Neural Information Retrieval (Neu-IR'17) August 07--11, 2017, Shinjuku, Tokyo, Japan
arxiv created 2017/07/19 · openalex publication_date 2017/07/19 · arxiv updated 2017/07/20 · openalex created_date 2017/07/31 · openalex updated_date 2026/07/28
Microblogging sites like Twitter and Weibo have emerged as important sourcesof real-time information on ongoing events, including socio-political events, emergency events, and so on. For instance, during emergency events (such as earthquakes, floods, terror attacks), microblogging sites are very useful for gathering situational information in real-time. During such an event, typically only a small fraction of the microblogs (tweets) posted are relevant to the information need. Hence, it is necessary to design effective methodologies for microblog retrieval, so that the relevant tweets can be automatically extracted from large sets of documents (tweets). In this work, we apply and compare various neural network-based IR models for microblog retrieval for a specific application, as follows. In a disaster situation, one of the primary and practical challenges in coordinating the post-disaster relief operations is to know about what resources are needed and what resources are available in the disaster-affected area. Thus, in this study, we focus on extracting these two specific types of microblogs or tweets namely need tweets and avail tweets, which are tweets which define some needs of the people and the tweets which offer some solutions or aid for the people, respectively.