scholarly journals Detecting a Risk Signal in Stock Investment Through Opinion Mining and Graph-Based Semi-Supervised Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 161943-161957
Author(s):  
Byungun Yoon ◽  
Yujin Jeong ◽  
Sunhye Kim
Keyword(s):  
Supervised Learning ◽  
Opinion Mining ◽  
Stock Investment

scholarly journals Feature Selection for Ordinal Text Classification

2014 ◽  
Vol 26 (3) ◽  
pp. 557-591 ◽  
Author(s):  
Stefano Baccianella ◽  
Andrea Esuli ◽  
Fabrizio Sebastiani
Keyword(s):  
Feature Selection ◽  
Supervised Learning ◽  
Opinion Mining ◽  
Rating Scale ◽  
Learning Algorithms ◽  
Learning Task ◽  
Support Vector ◽  
Data Sets ◽  
Product Review ◽  
Ordinal Classification

Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of estimating the rating of a data item on a fixed, discrete rating scale. This problem is receiving increased attention from the sentiment analysis and opinion mining community due to the importance of automatically rating large amounts of product review data in digital form. As in other supervised learning tasks such as binary or multiclass classification, feature selection is often needed in order to improve efficiency and avoid overfitting. However, although feature selection has been extensively studied for other classification tasks, it has not for ordinal classification. In this letter, we present six novel feature selection methods that we have specifically devised for ordinal classification and test them on two data sets of product review data against three methods previously known from the literature, using two learning algorithms from the support vector regression tradition. The experimental results show that all six proposed metrics largely outperform all three baseline techniques (and are more stable than these others by an order of magnitude), on both data sets and for both learning algorithms.

SeAbOM: Semi-supervised Learning for Aspect-Based Opinion Mining

2021 ◽  
pp. 479-489
Author(s):  
Sugandha C. Nandedkar ◽  
Jayantrao B. Patil
Keyword(s):  
Supervised Learning ◽  
Opinion Mining

Opinion Mining of Twitter Events using Supervised Learning

2018 ◽  
Vol 9 (2) ◽  
pp. 23-36 ◽  
Author(s):  
Nida Hakak ◽  
Mahira Kirmani
Keyword(s):  
Supervised Learning ◽  
Opinion Mining ◽  
Learning Algorithms ◽  
Rich Source ◽  
Competitive Performance ◽  
Daily Experience ◽  
Supervised Learning Algorithms ◽  
Context Features

Micro-blogs are a powerful tool to express an opinion. Twitter is one of the fastest growing micro-blogs and has more than 900 million users. Twitter is a rich source of opinion as users share their daily experience of life and respond to specific events using tweets on twitter. In this article, an automatic opinion classifier capable of automatically classifying tweets into different opinions expressed by them is developed. Also, a manually annotated corpus for opinion mining to be used by supervised learning algorithms is designed. An opinion classifier uses semantic, lexical, domain dependent, and context features for classification. Results obtained confirm competitive performance and the robustness of the system. Classifier accuracy is more than 75.05%, which is higher than the baseline accuracy.

scholarly journals Incorporating Expert-Based Investment Opinion Signals in Stock Prediction: A Deep Learning Framework

2020 ◽  
Vol 34 (01) ◽  
pp. 971-978
Author(s):  
Heyuan Wang ◽  
Tengjiao Wang ◽  
Yi Li
Keyword(s):  
Opinion Mining ◽  
Stock Prediction ◽  
Detection Model ◽  
Learning Framework ◽  
Movement Prediction ◽  
Stock Investment ◽  
Depth Analysis ◽  
Financial Domain ◽  
Social Media Platforms ◽  
The Impact

Investment messages published on social media platforms are highly valuable for stock prediction. Most previous work regards overall message sentiments as forecast indicators and relies on shallow features (bag-of-words, noun phrases, etc.) to determine the investment opinion signals. These methods neither capture the time-sensitive and target-aware characteristics of stock investment reviews, nor consider the impact of investor's reliability. In this study, we provide an in-depth analysis of public stock reviews and their application in stock movement prediction. Specifically, we propose a novel framework which includes the following three key components: time-sensitive and target-aware investment stance detection, expert-based dynamic stance aggregation, and stock movement prediction. We first introduce our stance detection model named MFN, which learns the representation of each review by integrating multi-view textual features and extended knowledge in financial domain to distill bullish/bearish investment opinions. Then we show how to identify the validity of each review, and enhance stock movement prediction by incorporating expert-based aggregated opinion signals. Experiments on real datasets show our framework can effectively improve the performance of both investment opinion mining and individual stock forecasting.

scholarly journals Semi-Supervised Target-Dependent Sentiment Classification for Micro-Blogs

2019 ◽  
Vol 19 (01) ◽  
pp. e06
Author(s):  
Shadi I. Abudalfa ◽  
Moataz A. Ahmed
Keyword(s):  
Social Media ◽  
Supervised Learning ◽  
Classification Accuracy ◽  
Opinion Mining ◽  
Semisupervised Learning ◽  
Sentiment Classification ◽  
Short Text ◽  
Learning Techniques ◽  
The Social ◽  
Learning Technique

The wealth of opinions available in the social media motivated researchers to develop automatic opinion detection tools. Many such tools are currently available online for opinion mining in short text, known as micro-blogs, but their efficacies are still limited. Current tools focus on detecting sentiment polarity expressed in a micro-blog regardless of the topic (target) discussed. Little improved approaches have been proposed to detect sentiment towards a specific target, referred to as target-dependent sentiment classification. Our literature review has shown that all these target-dependent approaches use supervised learning techniques. Such techniques need a huge amount of labeled data for increasing classification accuracy. However, preparing labeled data from social media needs a lot of efforts. In this work, we address this issue by employing semisupervised learning techniques that have not been used before with target-dependent sentiment classification. To the best of our knowledge, our work is the first research that employs semisupervised learning techniques in this direction. Semi-supervised learning techniques have been known in the literature to improve classification accuracy in comparison with supervised learning techniques; however, they use same number of labeled samples plus many unlabelled ones. In this work, we propose a new semi-supervised learning technique that uses less number of labeled microblogs than that used with supervised learning techniques. Experiment results have shown that the proposed technique provides competitive accuracy.

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