Predicting Cryptocurrency Value using Sentiment Analysis

Inamdar, Abid; Bhagtani, Aarti; Bhatt, Suraj K.; Shetty, Pooja M.

doi:10.1109/iccs45141.2019.9065838

Cited by 25 publications

(10 citation statements)

References 6 publications

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“…In this regard, peoples' sentiments and emotions can help in determining the up and down of cryptocurrency market value, especially, sentiment analysis is trendy nowadays for investment in cryptocurrency [1], [2]. Investors first perform an analysis of peoples' sentiment for a specific currency and then make investments according to the sentiments [3]. Because of that, sentiment analysis on cryptocurrency markets has become a task of great importance [4].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…Reference [76] refers to the impact of Twitter posts and news data on the price of blockchains, which announces their predictions on the prices.…”

Section: Miningmentioning

confidence: 99%

Methods of identifying unauthorized customers in cryptocurrency mining of distribution networks-a review

Najafzadeh

Gholami

2022

2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)

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“…However, these studies are dominated by Bitcoin-focused research considering aspects such as market efficiency (Kurihara & Fukushima, 2017), price dynamics and bubbles (Geuder et al, 2019), and portfolio management (Guesmi et al, 2019). Further, studies bifurcate towards sentiment analyses (Inamdar et al, 2019) or macroeconomic and financial indices (Poyser, 2019) as the main price drivers. Relatively few studies have examined altcoins (Wang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%