Real-Time Twitter Data Mining Approach to Infer User Perception Toward Active Mobility
Real-Time Twitter Data Mining Approach to Infer User Perception Toward Active Mobility
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DOI：
10.1177/03611981211004966
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发表时间：
2021-04
影响因子：
1.7
通讯作者：
Rezaur Rahman;Kazi Redwan Shabab;Kamol Chandra Roy;M. Zaki;Samiul Hasan
中科院分区：
文献类型：
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作者：
Rezaur Rahman;Kazi Redwan Shabab;Kamol Chandra Roy;M. Zaki;Samiul Hasan
本研究通过挖掘社交媒体中的地理标记数据并分析道路使用者的感知来评估共享交通设施的服务水平。提出了一种基于上下文理解的文本分类算法，过滤出与用户对主动移动性的感知相关的信息。使用基于关键字匹配的方法会产生大约75%的脱离上下文的推文，因此该方法被认为不适合从Twitter中提取信息。本研究实现了六种不同的文本分类模型，并比较了这些模型对推文分类的性能。该模型应用于真实世界的数据，以过滤出相关信息，并执行内容分析，以检查过滤后的数据中关键字的分布。文本分类模型“词频-逆文档频率”基于向量化的逻辑回归模型在分类推文方面表现最好。为了选择最佳模型，基于精确度、召回率、F1得分（精确度和召回率的几何平均值）和准确度指标来比较模型的性能。分析结果表明，所提出的方法可以帮助产生更多有关步行和骑自行车设施以及安全问题的信息。通过分析过滤后的数据的情感，可以推断DC区域中的自行车和步行设施的存在状况。这种方法可以是决策支持系统的关键部分，以了解现有的交通设施的服务质量水平。
This study evaluates the level of service of shared transportation facilities through mining geotagged data from social media and analyzing the perceptions of road users. An algorithm is developed adopting a text classification approach with contextual understanding to filter out relevant information related to users’ perceptions toward active mobility. Using a heuristic-based keyword matching approach produces about 75% tweets that are out of context, so that approach is deemed unsuitable for information extraction from Twitter. This study implements six different text classification models and compares the performance of these models for tweet classification. The model is applied to real-world data to filter out relevant information, and content analysis is performed to check the distribution of keywords within the filtered data. The text classification model “term frequency-inverse document frequency” vectorizer-based logistic regression model performed best at classifying the tweets. To select the best model, the performances of the models are compared based on precision, recall, F1 score (geometric mean of precision and recall), and accuracy metrics. The findings from the analysis show that the proposed method can help produce more relevant information on walking and biking facilities as well as safety concerns. By analyzing the sentiments of the filtered data, the existing condition of biking and walking facilities in the DC area can be inferred. This method can be a critical part of the decision support system to understand the qualitative level of service of existing transportation facilities.