Nistor, S.C., Moca, M., Moldovan, D., Oprean, D.B. & Nistor, R.L. (2021) Sensors [Q2]

Publicat: 18 Mai 2021

Nistor, S.C., Moca, M., Moldovan, D., Oprean, D.B. & Nistor, R.L. (2021) Building a Twitter Sentiment Analysis System with Recurrent Neural Networks. Sensors, 21(7), 2266.

DOI: https://doi.org/10.3390/s21072266

✓ Publisher: MDPI
✓ Web of Science Core Collection: Science Citation Index Expanded
✓ Categories: Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation
✓ Article Influence Score (AIS): 0.586 (2021) / Q2 in all categories

Abstract: This paper presents a sentiment analysis solution on tweets using Recurrent Neural Networks (RNNs). The method is can classifying tweets with an 80.74% accuracy rate, considering a binary task, after experimenting with 20 different design approaches. The solution integrates an attention mechanism aiming to enhance the network, with a two-way localization system: at memory cell level and at network level. We present an in-depth literature review for Twitter sentiment analysis and the building blocks that grounded the design decisions of our solution, employed as a core classification component within a sentiment indicator of the SynergyCrowds platform.

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