Skip to main content

Table 4 Accuracy results SemEval-2016 dataset. Results of the external systems from the SemEval-2016: task a competition are compared with the results of the neural network models presented in the current body of work. While a total of 34 systems contributed to the competition, only four systems are illustrated in descending order for comparison [52]

From: A deep neural network approach for sentiment analysis of medically related texts: an analysis of tweets related to concussions in sports

Model Layer/Filter Size Accuracy (%)
FFNN [400, 400]
[400, 400, 400]
[775, 225, 75, 25]
59.5
59.0
58.8
Single layer CNN [1, 2]
[1, 2, 3]
[3, 4, 5]
[1, 2, 3, 4, 5]
61.7
61.1
60.2
60.6
Multi-layer CNN [1, 2] 62.2
GRU [205] 60.6
LSTM [205] 61.2
Bi-Dir LSTM [205] 62.2
TCN [3] 59.6
External System Accuracy (%)
aueb.twitter.sentiment 62.9
sensei-lif 61.7
unimelb 61.6
senti-sys 60.9
Baseline 34.2