Sentiment Analysis Using Deep Learning: A Comparison Between Chinese And English

With the increasing popularity of opinion-rich resources, opinion mining and sentiment analysis has received increasing attention. Sentiment analysis is one of the most effective ways to find the opinion of authors. By mining what people think, sentiment analysis can provide the basis for decision making. Most of the objects of analysis are text data, such as Facebook status and movie reviews. Despite many sentiment classification models having good performance on English corpora, they are not good at Chinese or other languages. Traditional sentiment approaches impose many restrictions on the raw data, and they don't have enough capacity to deal with long-distance sequential dependencies. So, we propose a model based on recurrent neural network model using a context vector space model. Chinese information entropy is typically higher than English, we therefore hypothesise that context vector space model can be used to improve the accuracy of sentiment analysis. Our algorithm represents each complex input by a dense vector trained to translate sequence data to another sequence, like the translation of English and French. Then we build a recurrent neural network with the Long-Short-Term Memory model to deal the long-distance dependencies in input data, such as movie review. The results show that our approach has promise but still has a lot of room for improvement

Sentiment Analysis Based on Deep Learning: A Comparative Study

The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users' opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input feature

The impact of news narrative on the economy and financial markets

This thesis investigates the impact of news narrative on socio-economic systems across four experiments. Recent years have witnessed a rise in the use of so-called alternative data sources to model and predict dynamics in socio-economic systems. Notably, sources such as newspaper text allow researchers to quantify the elusive concept of narrative, to incorporate text-based features into forecasting frameworks and thus to evaluate the impact of narrative on economic events. The first experiment proposes a new method of incorporating a wide array of sentiment scores from global newspaper articles into macroeconomic forecasts, attempting to forecast industrial production and consumer prices leveraging narrative and sentiment from global newspapers. I model industrial production and consumer prices across a diverse range of economies using an autoregressive framework. The second experiment uses narrative from global newspapers to construct themebased knowledge graphs about world events, demonstrating that features extracted from such graphs improve forecasts of industrial production in three large economies. The third experiment proposes a novel method of including news themes and their associated sentiment into predictions of changes in breakeven inflation rates (BEIR) for eight diverse economies with mature fixed income markets. I utilise five types of machine learning algorithms incorporating narrative-based features for each economy. In the above experiments, models incorporating narrative-based features generally outperform their benchmarks that do not contain such variables, demonstrating the predictive power of features derived from news narrative. The fourth experiment utilises GDELT data and the filtering methodology introduced in the first experiment to create a profitable systematic trading strategy based on the average tone scores for 15 diverse economies

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization

Techniques for Stock Market Prediction: A Review

Stock market forecasting has long been viewed as a vital real-life topic in economics world. There are many challenges in stock market prediction systems such as the Efficient Market Hypothesis (EMH), Nonlinearity, complex, diverse datasets, and parameter optimization. A stock's value on the stock market fluctuates due to many factors like previous trends of the stock, the current news, twitter feeds, any online customer feedbacks etc. In this paper, the literature is critically analysed on approaches used for stock market prediction in terms of stock datasets, features used, evaluation metrics used, statistical, machine learning and deep learning techniques along with the directions for the future. The focus of this review is on trend and value prediction for stocks. Overall, 68 research papers have been considered for review from years 1998-2023. From the review, Indian stock market datasets are found to be most frequently used datasets. Evaluation metrics used commonly are accuracy and Mean Absolute Percentage Error. ARIMA is reported as the most used frequently statistical technique for stick market prediction. Long-Short Term Memory and Support Vector Machine are the commonly used algorithms in stock market prediction. The advantages and disadvantages of frequently used evaluation metrics, machine learning, deep learning and statistical approaches are also included in this survey

A State-of-the-Art Review of Time Series Forecasting Using Deep Learning Approaches

Time series forecasting has recently emerged as a crucial study area with a wide spectrum of real-world applications. The complexity of data processing originates from the amount of data processed in the digital world. Despite a long history of successful time-series research using classic statistical methodologies, there are some limits in dealing with an enormous amount of data and non-linearity. Deep learning techniques effectually handle the complicated nature of time series data. The effective analysis of deep learning approaches like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long short-term memory (LSTM), Gated Recurrent Unit (GRU), Autoencoders, and other techniques like attention mechanism, transfer learning, and dimensionality reduction are discussed with their merits and limitations. The performance evaluation metrics used to validate the model's accuracy are discussed. This paper reviews various time series applications using deep learning approaches with their benefits, challenges, and opportunities

Social media cross-source and cross-domain sentiment classification

Due to the expansion of Internet and Web 2.0 phenomenon, there is a growing interest in the sentiment analysis of freely opinionated text. In this paper, we propose a novel cross-source cross-domain sentiment classification, in which cross-domain labeled Web sources (Amazon and Tripadvisor) are used to train supervised learning models (including two deep learning algorithms) that are tested on typically non labeled social media reviews (Facebook and Twitter). We explored a three step methodology, in which dis- tinct balanced training, text preprocessing and machine learning methods were tested, using two languages: English and Italian. The best results were achieved when using undersampling training and a Convolutional Neural Network. Interesting cross-source classification performances were achieved, in particular when using Amazon and Tripadvisor reviews to train a model that is tested on Facebook data for both English and Italian.Research carried out with the support of resources of Big&Open Data Innovation Laboratory (BODaI-Lab), the University of Brescia, granted by Fondazione Cariplo and Regione Lombardia. The work of P. Cortez was supported by FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope UID/CEC/00319/2019. We would also like to thank the three anonymous reviewers for their helpful suggestions