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, Volume 77, Issue 24, pp 32213–32242 | Cite as

Deep neural network architecture for sentiment analysis and emotion identification of Twitter messages

  • Dario Stojanovski
  • Gjorgji Strezoski
  • Gjorgji Madjarov
  • Ivica Dimitrovski
  • Ivan Chorbev
Article
  • 124 Downloads

Abstract

In the work presented in this paper, we showcase a deep learning system for sentiment analysis and emotion identification in Twitter messages. The system consists of a convolutional neural network used for extracting features from textual data and a classifier for which we experiment with several different classifying algorithms. We train the network using pre-trained word embeddings obtained by unsupervised learning on large text corpora and compare the effectiveness of the different word vectors for this task. We evaluate our system on 3-class sentiment analysis with datasets provided by the Sentiment analysis in Twitter task from the SemEval competition. Additionally, we explore the effectiveness of our approach for emotion identification, by using an automatically annotated dataset with 7 distinct emotions. Our architecture achieves comparable performances to state-of-the-art techniques in the field of sentiment analysis and improves results in the field of emotion identification on the test we use in our evaluation. Moreover, the paper presents several use case scenarios, depicting real-world usage of our architecture.

Keywords

Twitter Convolutional neural networks Word embeddings Sentiment analysis Emotion identification 

Notes

Acknowledgements

We would like to acknowledge the support of the European Commission through the project MAESTRA Learning from Massive, Incompletely annotated, and Structured Data (Grant number ICT-2013-612944). Also, this work was partially financed by the Faculty of Computer Science and Engineering at the Ss. Cyril and Methodius University.

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Authors and Affiliations

  1. 1.Faculty of Computer Science and EngineeringSs. Cyril and Methodius UniversitySkopjeRepublic of Macedonia

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