Research Article
Monitoring the Emotional Response to the COVID-19 Pandemic Using Sentiment Analysis: A Case Study in Mexico
Table 1
Summary of massively distributed systems for performing sentiment analysis over large volumes of tweets.
| Reference | Tech | Batch/stream | Features | Comments |
| Victor and Lijo, 2019 [13] | Hadoop & Spark | Both | HBase interface | | Sathya et al., 2019 [14] | Hadoop | Batch | Classifier selection, preprocessing, sarcasm, VR | Open source, needs self-hosting | Bhuvaneswari et al., 2019 [2] | Hadoop & Kafka | Both | Uses flume | | Cenni et al., 2018 [7] | Hadoop | Batch | — | Aggregation of 4 projects | Sehgal and Agarwal, 2016 [15] | Hadoop | Batch | — | | Kumar and Kala, 2016 [16] | Mahout | Batch | — | Less complex to build, experiments made on a single node | Kumamoto, Wada, and Suzuki, 2014 [17] | | Batch | Graphs | No details | Khuc et al., 2012 [18] | Hadoop | Batch | HBase interface | Nice UI for data cleanup | Marcus et al., 2011 [19] | Hadoop | Batch | Peak detection, subevent selection | | This study | Microservices | Both | Autoscaling, easy to consume, cloud-native | Uses remote hosting |
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