Abstract
Financial news articles are believed to have impacts on stock price return. Previous works model news pieces in bag-of-words space, which analyzes the latent relationship between word statistical patterns and stock price movements.
1. Introduction
Stock market is an important and active part of nowadays financial market. Both investors and speculators in the market would like to make better profits by analyzing market information. Financial news articles, known as one major source of market information, are widely used and analyzed by investors.
3. Sentiment analysis on news impact
The stocks we investigate are listed in Hong Kong Stock Exchange.
4. Experiment results and discussion
In the experiment, we evaluate and compare six different approaches. We set up two approaches that use sentiment dictionaries, one approach that uses SenticNet and one approach that uses bag-of-words. Besides, we employ two traditional approaches that are based on the polarity asymmetry of the news sentiment.
5. Conclusion and future work
Financial news articles are believed to have impacts on stock price return. In this paper, we analyze the news impact from sentiment dimensions. We first implement a generic stock price prediction framework. Secondly, we use Harvard psychological dictionary and Loughran–McDonald financial sentiment dictionary to construct the sentiment dimensions.
What is an aspect based sentiment analysis?
Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task, which aims to detect target-aspect-sentiment elements in a sentence. Most of the existing research work distinguished the sentiment for aspects or targets independently, ignoring the corresponding relation between the targets and the aspects. However, such a corresponding relation is significant for the accurate prediction of fine-grained sentiment polarity. In this paper, we propose a novel end-to-end multiple-element joint detection model (MEJD), which effectively extracts all (target, aspect, sentiment) triples from a sentence. Our model utilizes BERT to obtain the initial embedding vector from the aspect–sentence joint input and applies bidirectional long short-term memory to model aspect and sentence representations. We then employ a graph convolutional network with attention mechanisms to capture the dependency relationship between aspect and sentence. We evaluate our approach on two restaurant datasets of SemEval 2015 Task 12 and SemEval 2016 Task 5. Experiment results show that our model achieves state-of-the-art performance in extracting (target, aspect, sentiment) triples. Moreover, the model also has good performance on multiple subtasks of target-aspect-sentiment detection.
How do stock prices change?
Stock prices change everyday by market forces (supply and demand). In recent years stock price prediction has been one of the most significant concern. Investors are investing on stock market on the basis of certain prediction. For prediction, stock market prices investors are applying some techniques and methods through which they get more profits and minimize their risks. Machine Learning methods are often used for the prediction of stock prices. This survey paper discusses various machine learning approaches (Supervised or Unsupervised) and methods through which the investors get to know the stock prices increase or decrease. It was done in five phases, such as data acquired, pre-processing of dataset, extraction of features, prediction of stock price using different techniques and display the result. In first phase, the data is collected from different social sites, historical data of companies. In second phase, the removal of incorrect, duplicate and dirt is done in pre-processing phase. In third phase, the reduction of data sets and the selection of useful data is done. In fourth phase, prediction is done using different machine learning techniques and approaches which is categorized as supervised and unsupervised learning techniques. Now, in last phase the accuracy is determined using different approaches.
What is sentic computing?
Sentic computing is a multi-disciplinary approach to sentiment analysis at the crossroads between affective computing and commonsense computing , which exploits both computer and social sciences to better recognize, interpret, and process opinions and sentiments over the Web. In the last ten years, many different models (such as the Hourglass of Emotions and Sentic Patterns), resources (such as AffectiveSpace and SenticNet), algorithms (such as Sentic LDA and Sentic LSTM), and applications (such as Sentic PROMs and Sentic Album) have been developed under the umbrella of sentic computing. In this paper, we review all such models, resources, algorithms, and applications together with the key shifts and tasks introduced by sentic computing in the context of affective computing and sentiment analysis. We also discuss future directions in these fields.
