Stock market prediction based on deep hybrid RNN model and sentiment analysis

John, A; Latha, T.

doi:10.1080/00051144.2023.2217602

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Figure 3 shows an analysis of evaluation metrics for existing models and the proposed model. (14) 0.981 Srijiranon et al (15) 0.965 Chen Y (16) 0.93 Proposed 0.985…”

Section: Resultsmentioning

confidence: 99%

Enhancing Stock Market Prediction: A Hybrid RNN-LSTM Framework with Sentiment Analysis

Kasture,

Shirsath

2024

IJST

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Objectives: Predicting stock prices with accuracy is a difficult but crucial endeavor for market participants. To increase the precision of stock market forecasts, this study suggests a novel method that blends sophisticated neural network algorithms with sentiment assessment. Methods: News data is pre-processed. Each text document received a sentiment score reflecting overall sentiment. These scores were integrated into the feature set, combining textual sentiment information with historical stock price data from BSE Sensex. The proposed model of hybrid RNN-LSTM is applied and compared with the Random Forest Regressor (RFR), and Support Vector Regressor (SVR). The LSTM model is also applied and tested on data without sentiment analysis scores. Findings: The proposed model yields promising results in stock market prediction accuracy. It significantly gives a low value for mean absolute error (0.036), mean squared error (0.021), and root mean square error (0.046) when compared with the SVR and RFR models. The R2 value is also compared with literature methods, and it shows a 0.40% to 5.5% enhancement in the scores. The results prove that the incorporation of sentiment analysis enriches the predictive capabilities of the model. Novelty: Sentiment analysis combined with the hybrid RNN-LSTM framework provides a new technique to increase the accuracy of stock market forecasts. Using sophisticated knowledge of market dynamics and sentiments, the proposed approach gives important results to market participants, investors, and analysts of financial markets. Keywords: Stock Market, Sentiment Analysis, Evaluation Metric, Prediction

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“…Figure 3 shows an analysis of evaluation metrics for existing models and the proposed model. (14) 0.981 Srijiranon et al (15) 0.965 Chen Y (16) 0.93 Proposed 0.985…”

Section: Resultsmentioning

confidence: 99%

Enhancing Stock Market Prediction: A Hybrid RNN-LSTM Framework with Sentiment Analysis

Kasture,

Shirsath

2024

IJST

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“…The forget gate generates an output value within the 0 to 1 range for each element within the prior cell state. An output of 1 indicates the intention to preserve the associated information, whereas an output of 0 signifies the intent to discard that information [37]. This representation of the forget gate is denoted in Equation ( 6).…”

Section: Prediction Model Using Long Short-term Memory (Lstm)mentioning

confidence: 99%

Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

Abdelfattah,

Darwish,

Elkaffas

2024

JTAER

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Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.

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Aspect based hotel recommendation system using dilated multichannel CNN and BiGRU with hyperbolic linear unit

Jai Arul Jose,

Mastan,

Al-Nuaimy

2024

Int. J. Mach. Learn. & Cyber.

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Stock market prediction based on deep hybrid RNN model and sentiment analysis

Cited by 6 publications

References 28 publications

Enhancing Stock Market Prediction: A Hybrid RNN-LSTM Framework with Sentiment Analysis

Enhancing Stock Market Prediction: A Hybrid RNN-LSTM Framework with Sentiment Analysis

Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

Aspect based hotel recommendation system using dilated multichannel CNN and BiGRU with hyperbolic linear unit

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