User and Topic Hybrid Context Embedding for Finance-Related Text Data Mining

Wang, Chenyu; Miao, Zhongchen; Lin, Yuefeng; Gao, Jian

doi:10.1109/icdmw.2019.00112

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…This clustering technique has been used successfully in big data analysis [45], [46]. In addition, it has been used in finance to cluster investment recommendations [47], [48], risk management [49], text data mining [50], stock data prediction model [51] and customer segmentation [52].…”

Section: B Birch Clusteringmentioning

confidence: 99%

Autoencoder-Enhanced Clustering: A Dimensionality Reduction Approach to Financial Time Series

Cortés,

Onieva,

López

et al. 2024

IEEE Access

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While Machine Learning significantly boosts the performance of predictive models, its efficacy varies across different data dimensions. It is essential to cluster time series data of similar characteristics, particularly in the financial sector. However, clustering financial time series data poses considerable challenges due to the market's inherent complexity and multidimensionality. To address these issues, our study introduces a novel clustering framework that leverages autoencoders for a compressed yet informative representation of financial time series. We rigorously evaluate our approach through multiple dimensionality reduction and clustering algorithms, applying it to key financial indices, including IBEX-35, CAC-40, DAX-30, S&P 500, and FTSE 100. Our findings consistently demonstrate that incorporating autoencoders significantly enhances the granularity and quality of clustering, effectively isolating distinct categories of financial time series. Our findings carry significant ramifications for the financial industry. By refining clustering methodologies, we set the stage for increasingly accurate financial predictive models, offering valuable insights for optimizing investment strategies and enhancing risk management.

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Section: B Birch Clusteringmentioning

confidence: 99%

Autoencoder-Enhanced Clustering: A Dimensionality Reduction Approach to Financial Time Series

Cortés,

Onieva,

López

et al. 2024

IEEE Access

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“…Word2vec [37,38] is the most popular technique in this field. It uses the conditional probability P(w|c) to predict the target word w based on its context c. They have been used for a variety of tasks, e.g., finance-relating text mining [47], question answering [51], biological sequences mining [2], and so on.…”

Section: Word and Sentence Embeddingmentioning

confidence: 99%

A machine learning-based approach to identify unlawful practices in online terms of service: analysis, implementation and evaluation

Guarino

Lettieri²,

Malandrino

et al. 2021

Neural Comput & Applic

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Terms of Service (ToS) are fundamental factors in the creation of physical as well as online legally relevant relationships. They not only define mutual rights and obligations but also inform users about contract key issues that, in online settings, span from liability limitations to data management and processing conditions. Despite their crucial role, however, ToS are often neglected by users that frequently accept without even reading what they agree upon, representing a critical issue when there exist potentially unfair clauses. To enhance users’ awareness and uphold legal safeguards, we first propose a definition of ToS unfairness based on a novel unfairness measure computed counting the unfair clauses contained in a ToS, and therefore, weighted according to their direct impact on the customers concrete interests. Secondly, we introduce a novel machine learning-based approach to classify ToS clauses, represented by using sentence embedding, in different categories classes and fairness levels. Results of a test involving well-known machine learning models show that Support Vector Machine is able to classify clauses into categories with a F1-score of 86% outperforming state-of-the-art methods, while Random Forest is able to classify clauses into fairness levels with a F1-score of 81%. With the final goal of making terms of service more readable and understandable, we embedded this approach into ToSware, a prototype of a Google Chrome extension. An evaluation study was performed to measure ToSware effectiveness, efficiency, and the overall users’ satisfaction when interacting with it.

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“…They showed that inputting distributed representations in addition to texts improves detection accuracy. Wang et al ( 2019 ) proposed a method to construct representations of users and topics from financial postings on Weibo. They applied the representations to sentiment analysis and showed that the accuracy was improved compared to the baseline.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Stock Price Trends by Analyzing Economic Reports With Analyst Profiles

Suzuki¹,

Sakaji²,

Izumi³

et al. 2022

Front. Artif. Intell.

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This article proposes a methodology to forecast the movements of analysts' estimated net income and stock prices using analyst profiles. Our methodology is based on applying natural language processing and neural networks in the context of analyst reports. First, we apply the proposed method to extract opinion sentences from the analyst report while classifying the remaining parts as non-opinion sentences. Then, we employ the proposed method to forecast the movements of analysts' estimated net income and stock price by inputting the opinion and non-opinion sentences into separate neural networks. In addition to analyst reports, we input analyst profiles to the networks. As analyst profiles, we used the name of an analyst, the securities company to which the analyst belongs, the sector which the analyst covers, and the analyst ranking. Consequently, we obtain an indication that the analyst profile effectively improves the model forecasts. However, classifying analyst reports into opinion and non-opinion sentences is insignificant for the forecasts.

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User and Topic Hybrid Context Embedding for Finance-Related Text Data Mining

Cited by 5 publications

References 13 publications

Autoencoder-Enhanced Clustering: A Dimensionality Reduction Approach to Financial Time Series

Autoencoder-Enhanced Clustering: A Dimensionality Reduction Approach to Financial Time Series

A machine learning-based approach to identify unlawful practices in online terms of service: analysis, implementation and evaluation

Forecasting Stock Price Trends by Analyzing Economic Reports With Analyst Profiles

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