基于CNN和BiLSTM的推文地理位置预测

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Data Science and Engineering Pub Date : 2021-01-01 Epub Date: 2021-07-08 DOI:10.1007/s41019-021-00165-1

Rhea Mahajan, Vibhakar Mansotra

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引用次数: 13

摘要

Twitter是最受欢迎的微博和社交网络平台之一，用户可以在280个字符以内以tweet的形式发布自己的观点、偏好、活动、想法、观点等。为了研究和分析一个地区用户的社会行为和活动，有必要确定推文的位置。本文旨在利用卷积神经网络和双向长短期记忆相结合的方法，通过提取推文内部特征和与推文相关的特征，对收集的30天的城市级实时推文进行地理定位预测。我们还将我们的结果与以前的基线模型进行了比较，我们的实验结果显示，在城市级预测中，基线方法的精度为92.6，中位误差为22.4公里，比基线方法有了显著的改进。

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Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM.

Twitter is one of the most popular micro-blogging and social networking platforms where users post their opinions, preferences, activities, thoughts, views, etc., in form of tweets within the limit of 280 characters. In order to study and analyse the social behavior and activities of a user across a region, it becomes necessary to identify the location of the tweet. This paper aims to predict geolocation of real-time tweets at the city level collected for a period of 30 days by using a combination of convolutional neural network and a bidirectional long short-term memory by extracting features within the tweets and features associated with the tweets. We have also compared our results with previous baseline models and the findings of our experiment show a significant improvement over baselines methods achieving an accuracy of 92.6 with a median error of 22.4 km at city level prediction.

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来源期刊

Data Science and Engineering Engineering-Computational Mechanics

CiteScore

10.40

自引率

2.40%

发文量

审稿时长

12 weeks

期刊介绍： The journal of Data Science and Engineering (DSE) responds to the remarkable change in the focus of information technology development from CPU-intensive computation to data-intensive computation, where the effective application of data, especially big data, becomes vital. The emerging discipline data science and engineering, an interdisciplinary field integrating theories and methods from computer science, statistics, information science, and other fields, focuses on the foundations and engineering of efficient and effective techniques and systems for data collection and management, for data integration and correlation, for information and knowledge extraction from massive data sets, and for data use in different application domains. Focusing on the theoretical background and advanced engineering approaches, DSE aims to offer a prime forum for researchers, professionals, and industrial practitioners to share their knowledge in this rapidly growing area. It provides in-depth coverage of the latest advances in the closely related fields of data science and data engineering. More specifically, DSE covers four areas: (i) the data itself, i.e., the nature and quality of the data, especially big data; (ii) the principles of information extraction from data, especially big data; (iii) the theory behind data-intensive computing; and (iv) the techniques and systems used to analyze and manage big data. DSE welcomes papers that explore the above subjects. Specific topics include, but are not limited to: (a) the nature and quality of data, (b) the computational complexity of data-intensive computing,(c) new methods for the design and analysis of the algorithms for solving problems with big data input,(d) collection and integration of data collected from internet and sensing devises or sensor networks, (e) representation, modeling, and visualization of big data,(f) storage, transmission, and management of big data,(g) methods and algorithms of data intensive computing, such asmining big data,online analysis processing of big data,big data-based machine learning, big data based decision-making, statistical computation of big data, graph-theoretic computation of big data, linear algebraic computation of big data, and big data-based optimization. (h) hardware systems and software systems for data-intensive computing, (i) data security, privacy, and trust, and(j) novel applications of big data.