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Spatiotemporal‐based sentiment analysis on tweets for risk assessment of event using deep learning approach
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2020-06-08 , DOI: 10.1002/spe.2851
M. Parimala 1 , R. M. Swarna Priya 1 , M. Praveen Kumar Reddy 1 , Chiranji Lal Chowdhary 1 , Ravi Kumar Poluru 2 , Suleman Khan 3
Affiliation  

Social media plays a vital role in analyzing the actual emotions of people after and during a disaster. Sentiment analysis is a method to detect a pattern from the emotions and feedback of the user. The main objective of the proposed work is to perform sentiment analysis on the tweets on a specific disaster context for a particular location at different intervals of time. LSTM network with word embedding algorithm is used to derive keywords based on the history of tweets and the context of the tweets. The proposed algorithm risk assessment sentiment analysis (RASA) uses the keywords generated from the network to classify the tweets and sentiment score for each location is identified. The model is validated with various state‐of‐art algorithms, namely, support vector machine, Naive‐Bayes, maximum entropy, logistic regression, random forest, XGBoost, stochastic gradient descent, and convolution neural networks in 2‐fold scenario: one for binary class and the other multiclass with three target classes. The results infer that the proposed RASA performs better in a binary class scenario with an increase of 1% when compared with XGBoost and 30% in multiclass scenario on an average when compared with all the other techniques. The model helps the government to take preventive measures to manage the posteffect of the disaster event in a location.

中文翻译:

使用深度学习方法对推文进行时空情感分析以评估事件风险

社交媒体在分析灾难后和灾难中人们的实际情感方面起着至关重要的作用。情感分析是一种从用户的情绪和反馈中检测模式的方法。拟议工作的主要目的是在不同时间间隔对特定位置的特定灾难情境下的推文进行情感分析。具有词嵌入算法的LSTM网络用于根据推文的历史记录和推文的上下文派生关键字。所提出的算法风险评估情感分析(RASA)使用从网络生成的关键字对推文进行分类,并识别每个位置的情感分数。该模型已通过各种最新算法进行了验证,包括支持向量机,朴素贝叶斯,最大熵,逻辑回归,随机森林,XGBoost,随机梯度下降和卷积神经网络在2种情况下:一个用于二元类,另一个用于三类目标类的多类。结果表明,所提出的RASA在二进制类方案中的性能更好,与XGBoost相比,增加了1%,在多类方案中,与所有其他技术相比,平均提高了30%。该模型可帮助政府采取预防措施来管理某个地点的灾难事件的后效应。结果表明,所提出的RASA在二进制类方案中的性能更好,与XGBoost相比,增加了1%,在多类方案中,与所有其他技术相比,平均提高了30%。该模型可帮助政府采取预防措施来管理某个地点的灾难事件的后效应。结果表明,所提出的RASA在二进制类方案中的性能更好,与XGBoost相比,增加了1%,在多类方案中,与所有其他技术相比,平均提高了30%。该模型可帮助政府采取预防措施来管理某个地点的灾难事件的后效应。
更新日期:2020-06-08
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