The Study on the Text Classification for Financial News Based on Partial Information

Wenju, Zhao; Zhang, Gaoyu; Yuan, George Xianzhi; Liu, Jun; Shan, Hongtao; Zhang, Shuyi

doi:10.1109/access.2020.2997969

Cited by 22 publications

(8 citation statements)

References 32 publications

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“…Kumar and Ravi [6] presented a survey of the applications of text mining in financial domain. Moreover, Zhao et al [7] presented a Study on the Text Classification for Financial News Based on Partial Information.…”

Section: Related Workmentioning

confidence: 99%

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Multilingual Financial Word Embeddings for Arabic, English and French

Zmandar

El-Haj

Rayson

2021

2021 IEEE International Conference on Big Data (Big Data)

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Natural Language Processing is increasingly being applied to analyse the text of many different types of financial documents. For many tasks, it has been shown that standard language models and tools need to be adapted to the financial domain in order to properly represent domain specific vocabulary, styles and meanings. Previous work has almost exclusively focused on English financial text, so in this paper we describe the creation of novel financial word embeddings for three languages: English, French and Arabic. In order to evaluate the effectiveness of the embeddings, we started by evaluating the English embeddings on a sentiment analysis classification task using the existing FinancialPhrase dataset and show improved performance over a standard GloVe based model using convolutional neural networks.

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Section: Related Workmentioning

confidence: 99%

“…For our experiments, we used the Financial phrasebank dataset 7 . The dataset was collected by [16].…”

Section: • Fns + Annual Reports Key Sections Corporamentioning

confidence: 99%

Multilingual Financial Word Embeddings for Arabic, English and French

Zmandar

El-Haj

Rayson

2021

2021 IEEE International Conference on Big Data (Big Data)

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“…This system was flexible and it worked for corpora of 4 different languages namely Italian, English, German and Spanish. Zhao et al [32] described how they used partial information to classify Chinese Financial News. Yang et al in their paper [31] discussed how much explainable transformer-based financial text classification models are.…”

Section: Problem Statementmentioning

confidence: 99%

Using Natural Language Processing to Understand Reasons and Motivators Behind Customer Calls in Financial Domain

Patil,

Chopra,

Ghosh

et al. 2021

Preprint

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In this era of abundant digital information, customer satisfaction has become one of the prominent factors in the success of any business. Customers want a one-click solution for almost everything. They tend to get unsatisfied if they have to call about something which they could have done online. Moreover, incoming calls are a high-cost component for any business. Thus, it is essential to develop a framework capable of mining the reasons and motivators behind customer calls. This paper proposes two models. Firstly, an attention-based stacked bidirectional Long Short Term Memory Network followed by Hierarchical Clustering for extracting these reasons from transcripts of inbound calls. Secondly, a set of ensemble models based on probabilities from Support Vector Machines and Logistic Regression. It is capable of detecting factors that led to these calls. Extensive evaluation proves the effectiveness of these models.

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“…T EXT classification is always a hot topic of Natural Language Processing (NLP), which is widely applied for text recognition and opinion extraction [1]- [4]. Currently, a large amount of the non-euclidean structure data, which can be quantified and analyzed, is generated by the social media every day, such as social network reviews, interview records, product reviews, email records, etc.…”

Section: Introductionmentioning

confidence: 99%

An Integration Model Based on Graph Convolutional Network for Text Classification

Tang

Xue

et al. 2020

IEEE Access

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Graph Convolutional Network (GCN) is extensively used in text classification tasks and performs well in the process of the non-euclidean structure data. Usually, GCN is implemented with the spatial-based method, such as Graph Attention Network (GAT). However, the current GCN-based methods still lack a more reasonable mechanism to account for the problems of contextual dependency and lexical polysemy. Therefore, an improved GCN (IGCN) is proposed to address the above problems, which introduces the Bidirectional Long Short-Term Memory (BiLSTM) Network, the Part-of-Speech (POS) information, and the dependency relationship. From a theoretical point of view, the innovation of IGCN is generalizable and straightforward: use the short-range contextual dependency and the longrange contextual dependency captured by the dependency relationship together to address the problem of contextual dependency and use a more comprehensive semantic information provided by the BiLSTM and the POS information to address the problem of lexical polysemy. What is worth mentioning, the dependency relationship is daringly transplanted from relation extraction tasks to text classification tasks to provide the graph required by IGCN. Experiments on three benchmarking datasets show that IGCN achieves competitive results compared with the other seven baseline models.

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The Study on the Text Classification for Financial News Based on Partial Information

Abstract: Her research interests include natural language understanding, Digital Control Technology, and deep learning.

Cited by 22 publications

References 32 publications

Multilingual Financial Word Embeddings for Arabic, English and French

Multilingual Financial Word Embeddings for Arabic, English and French

Using Natural Language Processing to Understand Reasons and Motivators Behind Customer Calls in Financial Domain

An Integration Model Based on Graph Convolutional Network for Text Classification

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