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Sentiment mapping: point pattern analysis of sentiment classified Twitter data

机译：情绪映射：情绪的点模式分析分类推特数据

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摘要

Detecting and monitoring collective public opinion via social media platforms can provide real-time information to researchers and policymakers. Human emotions, culture, and opinions can be tracked over time to understand where different sentiments manifest themselves geographically. Expanding on existing methodology, the present study draws from sentiment analysis and point pattern analysis to categorize and analyze sentiment toward natural gas across the United States as a means of applying these techniques together. Three methods of machine learning were used to classify collected tweets into positive and negative categories: Naive Bayes, Support Vector Machine, and Logistic Regression. Spatial clustering methods and spatial scan statistics were then applied to geocoded tweets to examine the distribution of sentiment about natural gas. In this analysis, the Logistic Regression and Support Vector Machine methods outperformed Naive Bayes in classifying sentiment. The different methods produced not rather different classification results but also produced varying geographic results. The spatial analyses successfully indicated persistent patterns of negative and positive tweeting about natural gas that correlate with expectations given the physical and cultural environment of various regions. Further, the temporal variation of geographic hotspots of sentiment was readily apparent, suggesting that these approaches can reveal dynamic sentiment landscapes.
机译：通过社交媒体平台检测和监测集体舆论可以为研究人员和政策制定者提供实时信息。随着时间的推移，可以跟踪人类的情感，文化和意见，了解不同的情绪在地理上表现出来的地方。在现有方法中扩展，本研究借鉴了情绪分析和点模式分析，分析了对美国对外天然气的对天然气的影响，作为将这些技术应用在一起的手段。使用三种机器学习方法将收集的推文分类为正面和负类：天真贝叶斯，支持向量机和逻辑回归。然后将空间聚类方法和空间扫描统计数据应用于地理编码推文，以检查天然气的情绪分布。在这种分析中，逻辑回归和支持向量机方法在分类情绪中表现出朴素的贝叶斯。不同的方法产生不相当不同的分类结果，但也产生了不同的地理结果。空间分析成功地表明了鉴于各个地区的物理和文化环境与期望相关的天然气的持续的阴性和积极推特模式。此外，情绪的地理热点的时间变化很容易明显，这表明这些方法可以揭示动态情绪景观。

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