Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data
| Autor: | Dionysis Goularas, Sani Kamis |
|---|---|
| Přispěvatelé: | Kamiş, S., Goularas, D., Yeditepe Üniversitesi |
| Rok vydání: | 2019 |
| Předmět: |
word embedding models
Word embedding Artificial neural network business.industry Computer science Twitter data Deep learning Sentiment analysis deep learning Machine learning computer.software_genre Convolutional neural network SemEval Recurrent neural network sentiment analysis convolutional neural networks Word2vec Artificial intelligence LSTM business computer |
| Zdroj: | 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). |
| DOI: | 10.1109/deep-ml.2019.00011 |
| Popis: | This study presents a comparison of different deep learning methods used for sentiment analysis in Twitter data. In this domain, deep learning (DL) techniques, which contribute at the same time to the solution of a wide range of problems, gained popularity among researchers. Particularly, two categories of neural networks are utilized, convolutional neural networks(CNN), which are especially performant in the area of image processing and recurrent neural networks (RNN) which are applied with success in natural language processing (NLP) tasks. In this work we evaluate and compare ensembles and combinations of CNN and a category of RNN the long short-term memory (LSTM) networks. Additionally, we compare different word embedding systems such as the Word2Vec and the global vectors for word representation (GloVe) models. For the evaluation of those methods we used data provided by the international workshop on semantic evaluation (SemEval), which is one of the most popular international workshops on the area. Various tests and combinations are applied and best scoring values for each model are compared in terms of their performance. This study contributes to the field of sentiment analysis by analyzing the performances, advantages and limitations of the above methods with an evaluation procedure under a single testing framework with the same dataset and computing environment. © 2019 IEEE. 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019 -- 26 August 2019 through 28 August 2019 -- -- 153122 |
| Databáze: | OpenAIRE |
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