Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages
- URL: http://arxiv.org/abs/2404.08665v1
- Date: Sat, 30 Mar 2024 16:46:25 GMT
- Title: Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages
- Authors: Silvia García-Méndez, Francisco de Arriba-Pérez, Ana Barros-Vila, Francisco J. González-Castaño,
- Abstract summary: We propose a novel Targeted Aspect-Based Emotion Analysis (TABEA) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet.
It is based on Natural Language Processing (NLP) techniques and Machine Learning streaming algorithms.
It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter.
- Score: 8.504685056067144
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Microblogging platforms, of which Twitter is a representative example, are valuable information sources for market screening and financial models. In them, users voluntarily provide relevant information, including educated knowledge on investments, reacting to the state of the stock markets in real-time and, often, influencing this state. We are interested in the user forecasts in financial, social media messages expressing opportunities and precautions about assets. We propose a novel Targeted Aspect-Based Emotion Analysis (TABEA) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet (instead of making an overall guess about that whole tweet). It is based on Natural Language Processing (NLP) techniques and Machine Learning streaming algorithms. The system comprises a constituency parsing module for parsing the tweets and splitting them into simpler declarative clauses; an offline data processing module to engineer textual, numerical and categorical features and analyse and select them based on their relevance; and a stream classification module to continuously process tweets on-the-fly. Experimental results on a labelled data set endorse our solution. It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter. To the best of our knowledge, no prior work in the literature has addressed this problem despite its practical interest in decision-making, and we are not aware of any previous NLP nor online Machine Learning approaches to TABEA.
Related papers
- Detection of Temporality at Discourse Level on Financial News by Combining Natural Language Processing and Machine Learning [8.504685056067144]
Finance-related news such as Bloomberg News, CNN Business and Forbes are valuable sources of real data for market screening systems.
We propose a novel system to detect the temporality of finance-related news at discourse level.
We have tested our system on a labelled dataset of finance-related news annotated by researchers with knowledge in the field.
arXiv Detail & Related papers (2024-03-30T16:40:10Z) - Detection of financial opportunities in micro-blogging data with a stacked classification system [6.817247544942709]
We propose a novel system to detect positive predictions in tweets.
Specifically, we seek a high detection precision to present a financial operator a substantial amount of such tweets.
We achieve it with a three-layer stacked Machine Learning classification system.
arXiv Detail & Related papers (2024-03-29T12:23:44Z) - AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data.
We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
arXiv Detail & Related papers (2024-03-19T09:45:33Z) - Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond [1.6622844933418388]
Deep learning based models are proposed to predict the trend of financial market based on NLP analysis of the twitter handles of leaders of different fields.
The Indian and USA financial markets are explored in the present work where as other markets can be taken in future.
arXiv Detail & Related papers (2024-03-18T18:19:08Z) - Decoding the Silent Majority: Inducing Belief Augmented Social Graph
with Large Language Model for Response Forecasting [74.68371461260946]
SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics.
Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
arXiv Detail & Related papers (2023-10-20T06:17:02Z) - Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases.
Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding.
This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
arXiv Detail & Related papers (2023-07-05T20:16:20Z) - ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information.
To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles.
Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
arXiv Detail & Related papers (2023-05-23T16:40:07Z) - Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines.
We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
arXiv Detail & Related papers (2023-04-15T19:22:37Z) - The Battle of Information Representations: Comparing Sentiment and
Semantic Features for Forecasting Market Trends [0.5249805590164902]
We study whether semantic features in the form of contextual embeddings are more valuable than sentiment attributes for forecasting market trends.
We consider the corpus of Twitter posts related to the largest companies by capitalization from NASDAQ and their close prices.
Our results show that in the substantially prevailing number of cases, the use of sentiment features leads to higher metrics.
arXiv Detail & Related papers (2023-03-24T18:30:15Z) - Identification of Twitter Bots based on an Explainable ML Framework: the
US 2020 Elections Case Study [72.61531092316092]
This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data.
Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm.
Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
arXiv Detail & Related papers (2021-12-08T14:12:24Z) - Using Twitter Attribute Information to Predict Stock Prices [0.0]
The model is based on a neural network with several layers of LSTM and fully connected layers.
It is trained with historical stock values, technical indicators and Twitter attribute information retrieved.
The results show that by adding more Twitter attributes, the MSE between the predicted prices and the actual prices improved by 3%.
arXiv Detail & Related papers (2021-05-04T10:27:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.