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"Sentiment": models, code, and papers

Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market

Feb 26, 2019
Rosdyana Mangir Irawan Kusuma, Trang-Thi Ho, Wei-Chun Kao, Yu-Yen Ou, Kai-Lung Hua

Stock market prediction is still a challenging problem because there are many factors effect to the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment and economic factors. This work explores the predictability in the stock market using Deep Convolutional Network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a Convolutional Neural Network model. This Convolutional Neural Network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of stock market. The effectiveness of our method is evaluated in stock market prediction with a promising results 92.2% and 92.1% accuracy for Taiwan and Indonesian stock market dataset respectively. The constructed model have been implemented as a web-based system freely available at http://140.138.155.216/deepcandle/ for predicting stock market using candlestick chart and deep learning neural networks.

* conference,13 pages,3 figures 

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The Natural Language Decathlon: Multitask Learning as Question Answering

Jun 20, 2018
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, Richard Socher

Deep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, dataset, and task. We introduce the Natural Language Decathlon (decaNLP), a challenge that spans ten tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, zero-shot relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution. We cast all tasks as question answering over a context. Furthermore, we present a new Multitask Question Answering Network (MQAN) jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. MQAN shows improvements in transfer learning for machine translation and named entity recognition, domain adaptation for sentiment analysis and natural language inference, and zero-shot capabilities for text classification. We demonstrate that the MQAN's multi-pointer-generator decoder is key to this success and performance further improves with an anti-curriculum training strategy. Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting. We also release code for procuring and processing data, training and evaluating models, and reproducing all experiments for decaNLP.


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Multi-channel CNN to classify nepali covid-19 related tweets using hybrid features

Mar 19, 2022
Chiranjibi Sitaula, Tej Bahadur Shahi

Because of the current COVID-19 pandemic with its increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected the developed countries but also developing countries such as Nepal. These complications can be understood from peoples' tweets/comments posted online after their proper analysis and sentiment classification. Nevertheless, owing to the limited number of tokens/words in each tweet, it is always crucial to capture multiple information associated with them for their better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag of words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel convolutional neural network (MCNN), which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral and negative) on NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification.

* This paper is under consideration in Journal of Ambient Intelligence and Humanized Computing (Springer) journal. This version may be deleted or updated at any time depending on the journal's policy upon acceptance 

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Transfer Learning Approach for Detecting Psychological Distress in Brexit Tweets

Jan 25, 2021
Sean-Kelly Palicki, Shereen Fouad, Mariam Adedoyin-Olowe, Zahraa S. Abdallah

In 2016, United Kingdom (UK) citizens voted to leave the European Union (EU), which was officially implemented in 2020. During this period, UK residents experienced a great deal of uncertainty around the UK's continued relationship with the EU. Many people have used social media platforms to express their emotions about this critical event. Sentiment analysis has been recently considered as an important tool for detecting mental well-being in Twitter contents. However, detecting the psychological distress status in political-related tweets is a challenging task due to the lack of explicit sentences describing the depressive or anxiety status. To address this problem, this paper leverages a transfer learning approach for sentiment analysis to measure the non-clinical psychological distress status in Brexit tweets. The framework transfers the knowledge learnt from self-reported psychological distress tweets (source domain) to detect the distress status in Brexit tweets (target domain). The framework applies a domain adaptation technique to decrease the impact of negative transfer between source and target domains. The paper also introduces a Brexit distress index that can be used to detect levels of psychological distress of individuals in Brexit tweets. We design an experiment that includes data from both domains. The proposed model is able to detect the non-clinical psychological distress status in Brexit tweets with an accuracy of 66% and 62% on the source and target domains, respectively.

* SAC 2021, MLA - Machine Learning and its Applications 

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Entity Retrieval and Text Mining for Online Reputation Monitoring

Jan 23, 2018
Pedro Saleiro

Online Reputation Monitoring (ORM) is concerned with the use of computational tools to measure the reputation of entities online, such as politicians or companies. In practice, current ORM methods are constrained to the generation of data analytics reports, which aggregate statistics of popularity and sentiment on social media. We argue that this format is too restrictive as end users often like to have the flexibility to search for entity-centric information that is not available in predefined charts. As such, we propose the inclusion of entity retrieval capabilities as a first step towards the extension of current ORM capabilities. However, an entity's reputation is also influenced by the entity's relationships with other entities. Therefore, we address the problem of Entity-Relationship (E-R) retrieval in which the goal is to search for multiple connected entities. This is a challenging problem which traditional entity search systems cannot cope with. Besides E-R retrieval we also believe ORM would benefit of text-based entity-centric prediction capabilities, such as predicting entity popularity on social media based on news events or the outcome of political surveys. However, none of these tasks can provide useful results if there is no effective entity disambiguation and sentiment analysis tailored to the context of ORM. Consequently, this thesis address two computational problems in Online Reputation Monitoring: Entity Retrieval and Text Mining. We researched and developed methods to extract, retrieve and predict entity-centric information spread across the Web.

* PhD Thesis 

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Learning Attitudes and Attributes from Multi-Aspect Reviews

Oct 31, 2012
Julian McAuley, Jure Leskovec, Dan Jurafsky

The majority of online reviews consist of plain-text feedback together with a single numeric score. However, there are multiple dimensions to products and opinions, and understanding the `aspects' that contribute to users' ratings may help us to better understand their individual preferences. For example, a user's impression of an audiobook presumably depends on aspects such as the story and the narrator, and knowing their opinions on these aspects may help us to recommend better products. In this paper, we build models for rating systems in which such dimensions are explicit, in the sense that users leave separate ratings for each aspect of a product. By introducing new corpora consisting of five million reviews, rated with between three and six aspects, we evaluate our models on three prediction tasks: First, we use our model to uncover which parts of a review discuss which of the rated aspects. Second, we use our model to summarize reviews, which for us means finding the sentences that best explain a user's rating. Finally, since aspect ratings are optional in many of the datasets we consider, we use our model to recover those ratings that are missing from a user's evaluation. Our model matches state-of-the-art approaches on existing small-scale datasets, while scaling to the real-world datasets we introduce. Moreover, our model is able to `disentangle' content and sentiment words: we automatically learn content words that are indicative of a particular aspect as well as the aspect-specific sentiment words that are indicative of a particular rating.

* 11 pages, 6 figures, extended version of our ICDM 2012 submission 

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A Scalable Framework for Multilevel Streaming Data Analytics using Deep Learning

Jul 15, 2019
Shihao Ge, Haruna Isah, Farhana Zulkernine, Shahzad Khan

The rapid growth of data in velocity, volume, value, variety, and veracity has enabled exciting new opportunities and presented big challenges for businesses of all types. Recently, there has been considerable interest in developing systems for processing continuous data streams with the increasing need for real-time analytics for decision support in the business, healthcare, manufacturing, and security. The analytics of streaming data usually relies on the output of offline analytics on static or archived data. However, businesses and organizations like our industry partner Gnowit, strive to provide their customers with real time market information and continuously look for a unified analytics framework that can integrate both streaming and offline analytics in a seamless fashion to extract knowledge from large volumes of hybrid streaming data. We present our study on designing a multilevel streaming text data analytics framework by comparing leading edge scalable open-source, distributed, and in-memory technologies. We demonstrate the functionality of the framework for a use case of multilevel text analytics using deep learning for language understanding and sentiment analysis including data indexing and query processing. Our framework combines Spark streaming for real time text processing, the Long Short Term Memory (LSTM) deep learning model for higher level sentiment analysis, and other tools for SQL-based analytical processing to provide a scalable solution for multilevel streaming text analytics.


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Stock Price Prediction Under Anomalous Circumstances

Sep 14, 2021
Jinlong Ruan, Wei Wu, Jiebo Luo

The stock market is volatile and complicated, especially in 2020. Because of a series of global and regional "black swans," such as the COVID-19 pandemic, the U.S. stock market triggered the circuit breaker three times within one week of March 9 to 16, which is unprecedented throughout history. Affected by the whole circumstance, the stock prices of individual corporations also plummeted by rates that were never predicted by any pre-developed forecasting models. It reveals that there was a lack of satisfactory models that could predict the changes in stocks prices when catastrophic, highly unlikely events occur. To fill the void of such models and to help prevent investors from heavy losses during uncertain times, this paper aims to capture the movement pattern of stock prices under anomalous circumstances. First, we detect outliers in sequential stock prices by fitting a standard ARIMA model and identifying the points where predictions deviate significantly from actual values. With the selected data points, we train ARIMA and LSTM models at the single-stock level, industry level, and general market level, respectively. Since the public moods affect the stock market tremendously, a sentiment analysis is also incorporated into the models in the form of sentiment scores, which are converted from comments about specific stocks on Reddit. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98% which can be used to optimize existing prediction methodologies.

* 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 4787-4794 
* 8 pages, 5 figures, 3 tables. 2020 IEEE International Conference on Big Data (Big Data) 

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BERTa├║: Ita├║ BERT for digital customer service

Jan 28, 2021
Paulo Finardi, Jos├ę Di├ę Viegas, Gustavo T. Ferreira, Alex F. Mansano, Vinicius F. Carid├í

In the last few years, three major topics received increased interest: deep learning, NLP and conversational agents. Bringing these three topics together to create an amazing digital customer experience and indeed deploy in production and solve real-world problems is something innovative and disruptive. We introduce a new Portuguese financial domain language representation model called BERTa\'u. BERTa\'u is an uncased BERT-base trained from scratch with data from the Ita\'u virtual assistant chatbot solution. Our novel contribution is that BERTa\'u pretrained language model requires less data, reached state-of-the-art performance in three NLP tasks, and generates a smaller and lighter model that makes the deployment feasible. We developed three tasks to validate our model: information retrieval with Frequently Asked Questions (FAQ) from Ita\'u bank, sentiment analysis from our virtual assistant data, and a NER solution. All proposed tasks are real-world solutions in production on our environment and the usage of a specialist model proved to be effective when compared to Google BERT multilingual and the DPRQuestionEncoder from Facebook, available at Hugging Face. The BERTa\'u improves the performance in 22% of FAQ Retrieval MRR metric, 2.1% in Sentiment Analysis F1 score, 4.4% in NER F1 score and can also represent the same sequence in up to 66% fewer tokens when compared to "shelf models".

* 10 pages, 2 figures, 6 tables, 9 equations, 27 references 

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Learning Rewards from Linguistic Feedback

Sep 30, 2020
Theodore R. Sumers, Mark K. Ho, Robert D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths

We explore unconstrained natural language feedback as a learning signal for artificial agents. Humans use rich and varied language to teach, yet most prior work on interactive learning from language assumes a particular form of input (e.g. commands). We propose a general framework which does not make this assumption. We decompose linguistic feedback into two components: a grounding to $\textit{features}$ of a Markov decision process and $\textit{sentiment}$ about those features. We then perform an analogue of inverse reinforcement learning, regressing the teacher's sentiment on the features to infer their latent reward function. To evaluate our approach, we first collect a corpus of teaching behavior in a cooperative task where both teacher and learner are human. We use our framework to implement two artificial learners: a simple "literal" model and a "pragmatic" model with additional inductive biases. We baseline these with a neural network trained end-to-end to predict latent rewards. We then repeat our initial experiment pairing human teachers with our models. We find our "literal" and "pragmatic" models successfully learn from live human feedback and offer statistically-significant performance gains over the end-to-end baseline, with the "pragmatic" model approaching human performance on the task. Inspection reveals the end-to-end network learns representations similar to our models, suggesting they reflect emergent properties of the data. Our work thus provides insight into the information structure of naturalistic linguistic feedback as well as methods to leverage it for reinforcement learning.

* 9 pages, 4 figures 

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