Investor sentiment identification based on the universum SVM

Long, Wen; Tang, Ye-ran; Tian, Yingjie

doi:10.1007/s00521-016-2684-y

Cited by 25 publications

(5 citation statements)

References 36 publications

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“…In addition to the leading financial websites, general financial reports or financial reports of specific companies were surveyed, such as Feuerriegel and Gordon (2018). Another section of researchers examined fewer formal sources of textual information, such as Li et al (2020), Ren et al (2019), Long et al (2018), in their work, they looked at the text published and discussed at Eastmoney, which is the largest news exchange in China. It is a very popular Chinese economic website, which has the largest number of visitors of all financially oriented websites in China.…”

Section: Sources Of Text Unstructured Datamentioning

confidence: 99%

Critical Review of Text Mining and Sentiment Analysis for Stock Market Prediction

Janková

2023

Journal of Business Economics and Management

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The paper is aimed at a critical review of the literature dealing with text mining and sentiment analysis for stock market prediction. The aim of this work is to create a critical review of the literature, especially with regard to the latest findings of research articles in the selected topic strictly focused on stock markets represented by stock indices or stock titles. This requires examining and critically analyzing the methods used in the analysis of sentiment from textual data, with special regard to the possibility of generalization and transferability of research results. For this reason, an analytical approach is also used in working with the literature and a critical approach in its organization, especially for completeness, coherence, and consistency. Based on the selected criteria, 260 articles corresponding to the subject area are selected from the world databases of Web of Science and Scopus. These studies are graphically captured through bibliometric analysis. Subsequently, the selection of articles was narrowed to 49. The outputs are synthesized and the main findings and limits of the current state of research are highlighted with possible future directions of subsequent research.

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Section: Sources Of Text Unstructured Datamentioning

confidence: 99%

Critical Review of Text Mining and Sentiment Analysis for Stock Market Prediction

Janková

2023

Journal of Business Economics and Management

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“…Huq et al [36] used SVM and k-nearest neighbors (KNN) algorithms to analyze the sentiment of twitter data. Long et al [37] used SVM to classify stock forum posts using additional samples containing prior knowledge. Although the machine learning-based method can automatically extract features, it often relies on manual feature selection.…”

Section: B Sentiment Analysis Based On Machine Learningmentioning

confidence: 99%

Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning

et al. 2020

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In recent years, with the rapid development of Internet technology, online shopping has become a mainstream way for users to purchase and consume. Sentiment analysis of a large number of user reviews on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning technology, and overcomes the shortcomings of existing sentiment analysis model of product reviews. The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews. Then the CNN and the Gated Recurrent Unit (GRU) network are used to extract the main sentiment features and context features in the reviews and use the attention mechanism to weight. And finally classify the weighted sentiment features. In terms of data, this paper crawls and cleans the real book evaluation of dangdang.com, a famous Chinese e-commerce website, for training and testing, all of which are based on Chinese. The scale of the data has reached 100000 orders of magnitude, which can be widely used in the field of Chinese sentiment analysis. The experimental results show that the model can effectively improve the performance of text sentiment analysis.

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“…On the other side, deep neural networks such as CNNs can extract high-level features from the images [13][14][15][16]. And the high-level features including more semantic information is good at image sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

“…So, CNNs have been widely used in image sentiment classification and show certainly an improvement. Among the CNNs, very deep convolutional networks for large-scale image recognition known as VGGNet shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to [16][17][18][19] weight layers [17]. So, VGGNet and fine-tuned VGGNet with 16 layers or 19 layers have been widely used for extracting deep features of images and improve the classification performance of image sentiment [7,8,18].…”

Section: Related Workmentioning

confidence: 99%

Region-based convolutional neural network using group sparse regularization for image sentiment classification

Xiong

Liu

Song

et al. 2019

J Image Video Proc.

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As an information carrier with rich semantics, images contain more sentiment than texts and audios. So, images are increasingly used by people to express their opinions and sentiments in social network. The sentiments of the images are overall and should come from different regions. So, the recognition of the sentiment regions will help to concentrate on important factors the affect the sentiments. Meanwhile, deep learning method for image sentiment classification needs simple and efficient approach for simultaneously carrying out pruning and feature selection whilst optimizing the weights. Motivated by these observations, we design a region-based convolutional neural network using group sparse regularization for image sentiment classification: R-CNNGSR. The method obtains the initial sentiment prediction model through CNN using group sparse regularization to get compact neural network, and then automatically detect the sentiment regions by combining the underlying features and sentimental features. Finally, the whole image and the sentiment region are fused to predict the overall sentiment of the images. Experiment results demonstrate that our proposed R-CNNGSR significantly outperforms the state-ofthe-art methods in image sentiment classification.

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Investor sentiment identification based on the universum SVM

Cited by 25 publications

References 36 publications

Critical Review of Text Mining and Sentiment Analysis for Stock Market Prediction

Critical Review of Text Mining and Sentiment Analysis for Stock Market Prediction

Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning

Region-based convolutional neural network using group sparse regularization for image sentiment classification

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