Novel Sentiment Analysis from Twitter for Stock Change Prediction

Cui, Yang; Jiang, Yucen; Gu, Haisong

doi:10.1007/978-981-19-8991-9_13

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…Leveraging sentiment extracted from social media, specifically Twitter, successfully predicted the 2016 US election results with an 81% model accuracy [10]. Similarly, Bollen achieved an accuracy of 87.6% in predicting the Dow Jones Index using various emotional dimensions extracted from tweets, reinforcing that prediction of the stock market is possible [11].…”

Section: The Methods and Results Of Previous Researchmentioning

confidence: 76%

Implementing NLP techniques and data for Europe financial market forecasting

Su,

Xiang,

Lai

et al. 2023

ACE

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The large military conflict between Russia and Ukraine has consequences for the global financial market. The European region is one of the hardest hits, which experienced a high energy cost and devaluation of the currency. This paper investigates the Employment Expectations Indicator (EEI) and Economic Sentiment Indicator (ESI) from the business consumer survey index to reflect the reaction of the consumers of the European market by implementing Natural Language Processing (NLP) techniques and machine learning. In the object, the monetary policy decisions from European Central Bank are considered the data set for forecasting the Index. In the result, all nine models from scikit-learn contributed a great job in both categories of Accuracy, Precision, and F-score and Recall with a level of 90% as average. It represents that the NLP techniques effectively forecast the future value of two European business and consumer indexes. However, the limit of the dataset and model mostly related to the performance of NLP techniques is working for forecasting but less related to the expected values of the EEI and ESI indices. Future research would focus more on forecasting the expected value of the two indexes and reflect the consumers' reactions in the markets.

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Section: The Methods and Results Of Previous Researchmentioning

confidence: 76%

Implementing NLP techniques and data for Europe financial market forecasting

Su,

Xiang,

Lai

et al. 2023

ACE

View full text Add to dashboard Cite

show abstract

“…To improve the model's ability to capture intricate linguistic features and context, researchers have explored the use of an attention-based mechanism in sentiment analysis for Twitter. • Transformers-based models, such as BERT (Bidirectional Encoder Representations from Transformers) [126,127], RoBERTa (Robustly Optimized BERT pretraining approach) [128], XLNet (eXtreme MultiLingual Language Model) [129], and GPT (Generative Pre-trained Transformer) [130], has been fine-tuned for sentiment analysis on Twitter data. These models use attention mechanisms to weigh the importance of different words in a text and can identify key patterns and relationships between words, making them particularly effective for analyzing short and noisy text like Twitter posts.…”

Section: B Literature Surveymentioning

confidence: 99%

A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

Chaudhary,

Girdhar,

Sharma

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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Microblogging site Twitter is one of the most influential online social media websites, that offers a platform for the masses to communicate, express their opinions, and share information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers, who seek to understand and analyze the sentiments contained within this massive user-generated text. The task of sentiment analysis entails extracting and identifying user opinions from the text, and various lexicon and machine learning-based methods have been developed over the years to accomplish this. However, deep learning-based approaches have recently become dominant due to their superior performance. The current study briefs on standard preprocessing techniques and various word embeddings for data preparation. It then delves into a taxonomy to provide a comprehensive summary of deep learning-based approaches. Additionally, the work compiles popular benchmark datasets and highlights evaluation metrics employed for performance measures as well as the resources available in the public domain to aid sentiment analysis tasks. Furthermore, the survey discusses domain-specific practical applications of sentiment analysis tasks. Finally, the study concludes with various research challenges and outlines future outlooks for further investigation.

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Novel Sentiment Analysis from Twitter for Stock Change Prediction

Cited by 2 publications

References 31 publications

Implementing NLP techniques and data for Europe financial market forecasting

Implementing NLP techniques and data for Europe financial market forecasting

A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

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