Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model

Abstract

The cryptocurrency market has been developed at an unprecedented speed over the past few years. Cryptocurrency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Although cryptocurrency ensures legitimate and unique transactions by utilizing cryptographic methods, this industry is still in its inception and serious concerns have been raised about its use. Analysis of the sentiments about cryptocurrency is highly desirable to provide a holistic view of peoples' perceptions. In this regard, this study performs both sentiment analysis and emotion detection using the tweets related to the cryptocurrency which are widely used for predicting the market prices of cryptocurrency. For increasing the efficacy of the analysis, a deep learning ensemble model LSTM-GRU is proposed that combines two recurrent neural networks applications including long short term memory (LSTM) and gated recurrent unit (GRU). LSTM and GRU are stacked where the GRU is trained on the features extracted by LSTM. Utilizing term frequency-inverse document frequency, word2vec, and bag of words (BoW) features, several machine learning and deep learning approaches and a proposed ensemble model are investigated. Furthermore, TextBlob and Text2Emotion are studied for emotion analysis with the selected models. Comparatively, a larger number of people feel happy with the use of cryptocurrency, followed by fear and surprise emotions. Results suggest that the performance of machine learning models is comparatively better when BoW features are used. The proposed LSTM-GRU ensemble shows an accuracy of 0.99 for sentiment analysis, and 0.92 for emotion prediction and outperforms both machine learning and state-of-the-art models.

A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis

“…Deep learning-based methods have also been applied to the sentiment analysis domain recently to further improve efficiency [ 1 , 4 , 5 , 10 , 12 , 14 , 20 , 25 , 26 , 29 , 30 , 52 ].…”

“…Based on tweets related to cryptocurrency, the work proposed by Aslam et al [ 4 ] performs sentiment analysis as well as emotion detection for forecasting cryptocurrency market value. To increase the efficiency of the analysis, LSTM-GRU is proposed, a deep learning ensemble model that combines the features of two different RNN applications, including LSTM and Gated Recurrent Unit (GRU).…”

The Impact of Omicron Variant in Vaccine Uptake in South Africa

“…This approach could be likened to be similar as the study in 39, 59, 60 . For example, in Naila et al 59 , the Text2Emotion model was used to detect emotions on cryptocurrency related tweets. Here, Text2Emotion model was used to identify the embedded emotion on cryptocurrency related tweets and presented the output in the form of a dictionary, classifying tweets as Happy, Sadness, Anger, Surprise, or Fear.…”

A Review on Emotion and Sentiment analysis Using Learning Techniques

“…[15]. (Aslam et al, 2022) The research employs cryptocurrency-related tweets for sentiment analysis & emotion recognition, which are widely used for projecting bitcoin market prices. As a method of boosting the speed of the analysis, the LSTM-GRU deep learning ensembles model was constructed.…”

“…The suggested LSTM-GRU ensemble outperforms both machine learning or state-of-the-art models, with an efficiency of 0.99 for sentiment and 0.92 for emotion prediction. Both of these outcomes are outstanding [20]. (Polonijo et al, 2021) The goal of this research is to present a deep learning method for combining sentiment ratings with Word2Vec vectors.…”