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

Aim:

          The sentiment analysis for crypto currency-related tweets, Crypto currency market price prediction based on the analyzed sentiments with the help of LSTM Long short-term memory.

Abstract:

          The crypto currency market has been developed at an unprecedented speed over the past few years. Crypto currency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Although crypto currency 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 crypto currency 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 crypto currency which are widely used for predicting the market prices of crypto currency. 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, Text Blob and Text2Emotion are studied for emotion analysis with the selected models. Comparatively, a larger number of people feel happy with the use of crypto currency, followed by fear and surprise emotions. Results suggest that the performance of machine learning models is comparatively better when BoW features are used.

 Existing System:

            This study performs experiments for sentiment analysis and emotion detection on crypto currency related tweets using Machine Learning algorithms.

 Problem definition:

          Binary classifiers are used to distinguish tweets with emotions and tweets without emotions. Two main tasks of the approach include offline training and online classification task.

 Proposed System:

          By using machine learning algorithms we got better accuracy but not best accuracy. This study performs experiments for sentiment analysis and emotion detection on crypto currency related tweets using BI-LSTM and GRU.

 Advantage:

          As a result, the BiLSTM model is beneficial in some NLP tasks, such as sentence classification, translation, and entity recognition. The Bi-LSTM algorithm able to predict missing word of next sentence based on previous sentence.