LSTM-based sentiment analysis for stock price forecast

Ko, Ching Ru; Chang, Hsien Tsung

doi:10.7717/peerj-cs.408

Cited by 44 publications

(25 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We reviewed lexicon and machine learning approaches to sentiment analysis classification on their performance. Sentiment analysis is described in References 7,18‐20 as a technique that uses NLP, data mining and statistical methods to extract the users' opinions and sentiments from social media data, for instance, tweets and other online resources like websites. It can be used to establish and understand how an audience reacts to a brand or issue either positively, negatively or in a neutral way from unstructured and unorganized textual content of websites and social media resources.…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis on social media tweets using dimensionality reduction and natural language processing

Omuya

Okeyo

Kimwele

2022

Engineering Reports

View full text Add to dashboard Cite

Social media has been embraced by different people as a convenient and official medium of communication. People write or share messages and attach images and videos on Twitter, Facebook and other social media platforms. It therefore generates a lot of data that is rich in sentiments. Sentiment analysis has been used to determine the opinions of clients, for instance, relating to a particular product or company. Lexicon and machine learning approaches are the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is, however, distorted by noise, the curse of dimensionality, the data domains and the size of data used for training and testing. This article aims at developing a model for sentiment analysis of social media data in which dimensionality reduction and natural language processing with part of speech tagging are incorporated. The model is tested using Naïve Bayes, support vector machine, and K‐nearest neighbor algorithms, and its performance compared with that of two other sentiment analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.

show abstract

Section: Related Workmentioning

confidence: 99%

Sentiment analysis on social media tweets using dimensionality reduction and natural language processing

Omuya

Okeyo

Kimwele

2022

Engineering Reports

View full text Add to dashboard Cite

show abstract

“…For ABCDM bidirectional layer’s output, an attention model is used to emphasize various words simultaneously. To analyze text sentiments ( Ko & Chang, 2021 ) used the NLP tool BERT and LSTM for evaluating time series data to anticipate the stock price using stock transaction history and text sentiments. This model improved the average root-mean-square error (RMSE) accuracy by 12.05.…”

Section: Related Literaturementioning

confidence: 99%

Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble

Yenkikar

Babu

Hemanth

2022

PeerJ Computer Science

View full text Add to dashboard Cite

The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features. The model employs the Cascaded Feature Selection (CFS) strategy, a novel statistical assessment approach based on Wilcoxon rank sum test, univariate logistic regression assisted significant predictor test and cross-correlation test. It further uses the efficacy of word2vec-based continuous bag-of-words and n-gram feature extraction in conjunction with SentiWordNet for finding optimal features for classification. We experiment on six public Twitter sentiment datasets, the STS-Gold dataset, the Obama-McCain Debate (OMD) dataset, the healthcare reform (HCR) dataset and the SemEval2017 Task 4A, 4B and 4C on a heterogeneous classifier ensemble comprising fourteen individual classifiers from different paradigms. Results from the experimental study indicate that CFS supports in attaining a higher classification accuracy with up to 50% lesser features compared to count vectorizer approach. In Intra-model performance assessment, the Artificial Neural Network-Gradient Descent (ANN-GD) classifier performs comparatively better than other individual classifiers, but the Best Trained Ensemble (BTE) strategy outperforms on all metrics. In inter-model performance assessment with existing state-of-the-art systems, the proposed model achieved higher accuracy and outperforms more accomplished models employing quantum-inspired sentiment representation (QSR), transformer-based methods like BERT, BERTweet, RoBERTa and ensemble techniques. The research thus provides critical insights into implementing similar strategy into building more generic and robust expert system for sentiment analysis that can be leveraged across industries.

show abstract

“…Investors will expect the stock price to rise or fall according to information they could obtain, such as financial news and web comments. Therefore, analysing financial textual content can recognise the sentiment of the market and trading willingness, and then predict the behaviour of investors ( Ko & Chang, 2021 ). Prior studies in finance textual content analysis suggest that financial articles and social media comments can be applied to predict stock prices ( Tetlock, Saar-Tsechansky & Macskassy, 2008 ).…”

Section: Related Workmentioning

confidence: 99%

Prediction of stock price direction using the LASSO-LSTM model combines technical indicators and financial sentiment analysis

Yang

Wang

2022

PeerJ Computer Science

View full text Add to dashboard Cite

Correctly predicting the stock price movement direction is of immense importance in the financial market. In recent years, with the expansion of dimension and volume in data, the nonstationary and nonlinear characters in finance data make it difficult to predict stock movement accurately. In this article, we propose a methodology that combines technical analysis and sentiment analysis to construct predictor variables and then apply the improved LASSO-LASSO to forecast stock direction. First, the financial textual content and stock historical transaction data are crawled from websites. Then transfer learning Finbert is used to recognize the emotion of textual data and the TTR package is taken to calculate the technical indicators based on historical price data. To eliminate the multi-collinearity of predictor variables after combination, we improve the long short-term memory neural network (LSTM) model with the Absolute Shrinkage and Selection Operator (LASSO). In predict phase, we apply the variables screened as the input vector to train the LASSO-LSTM model. To evaluate the model performance, we compare the LASSO-LSTM and baseline models on accuracy and robustness metrics. In addition, we introduce the Wilcoxon signed rank test to evaluate the difference in results. The experiment result proves that the LASSO-LSTM with technical and sentiment indicators has an average 8.53% accuracy improvement than standard LSTM. Consequently, this study proves that utilizing historical transactions and financial sentiment data can capture critical information affecting stock movement. Also, effective variable selection can retain the key variables and improve the model prediction performance.

show abstract

LSTM-based sentiment analysis for stock price forecast

Cited by 44 publications

References 15 publications

Sentiment analysis on social media tweets using dimensionality reduction and natural language processing

Sentiment analysis on social media tweets using dimensionality reduction and natural language processing

Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble

Prediction of stock price direction using the LASSO-LSTM model combines technical indicators and financial sentiment analysis

Contact Info

Product

Resources

About