Analyzing and Predicting the US Midterm Elections on Twitter with Recurrent Neural Networks

Lopardo, Antonio; Brambilla, Marco

doi:10.1109/bigdata.2018.8622441

Cited by 3 publications

(1 citation statement)

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“…Moreover, neural network application on NLP has drawn attention recently. Reference [44] have applied Recurrent Neural Network (RNN) with word2vec word embedding to classify Tweets relating to US political parties' competition in mid-term elections. Through locating Tweets in 68 most competitive districts, system could predict the winner with 60% accuracy.…”

Section: B Related Workmentioning

confidence: 99%

Mapping Consumer Sentiment Toward Wireless Services Using Geospatial Twitter Data

Procter

Zhang

et al. 2019

IEEE Access

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Hyper-dense wireless network deployment is one of the popular solutions to meeting high capacity requirement for 5G delivery. However, current operator understanding of consumer satisfaction comes from call centers and base station quality-of-service (QoS) reports with poor geographic accuracy. The dramatic increase in geo-tagged social media posts adds a new potential to understand consumer satisfaction towards target-specific quality-of-experience (QoE) topics. In our paper, we focus on evaluating users' opinion on wireless service-related topics by applying natural language processing (NLP) to geo-tagged Twitter data. Current generalized sentiment detection methods with generalized NLP corpora are not topic specific. Here, we develop a novel wireless service topic-specific sentiment framework, yielding higher targeting accuracy than generalized NLP frameworks. To do so, we first annotate a new sentiment corpus called SignalSentiWord (SSW) and compare its performance with two other popular corpus libraries, AFINN and SentiWordNet. We then apply three established machine learning methods, namely: Naïve Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network (RNN) to build our topic-specific sentiment classifier. Furthermore, we discuss the capability of SSW to filter noisy and high-frequency irrelevant words to improve the performance of machine learning algorithms. Finally, the real-world testing results show that our proposed SSW improves the performance of NLP significantly.INDEX TERMS Wireless, quality of experience, natural language processing, social media data, consumer.

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Section: B Related Workmentioning

confidence: 99%