Forecasting Bitcoin price using time opinion mining and bi-directional GRU

2021 ◽  
pp. 1-9
Author(s):  
Sumaiya Begum Akbar ◽  
Valarmathi Govindarajan ◽  
Kalaiselvi Thanupillai
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Positive Impact ◽  
Percentage Error ◽  
Price Forecast ◽  
Price Prediction ◽  
Short Memory ◽  
Time Series Method ◽  
Gated Recurrent Unit ◽  
Hourly Basis

Bitcoin is an innovative decentralized digital currency without intermediaries. Bitcoin price prediction is a demanding need in the present situation. This paper makes an investigation on the Bitcoin price forecast with a Bi-directional Gated Recurrent Unit (GRU) time series method, combined with opinion mining based on Twitter and Reddit feeds. An hourly basis sentimental analysis through the implementation of Natural Language Processing presents a positive impact of sentimental analysis on the Bitcoin price prediction. For prediction, RNN, long-short memory, GRU has been utilized. Unidirectional and Bi-directional versions of all three networks with and without sentimental analysis were implemented for comparison. Of all the techniques implemented Bi-directional GRU along with sentimental analysis gives a minimum RMSE and Minimum absolute percentage error of 1108.33 and 7.384%. Thus, the framework including Bi-Directional GRU along with Sentimental Analysis provides better results than the State-of-art methods.

scholarly journals A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms

AI ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 477-496
Author(s):  
Mohammad J. Hamayel ◽  
Amani Yousef Owda
Keyword(s):  
Trading Volume ◽  
Prediction Models ◽  
Short Term Memory ◽  
Price Volatility ◽  
Machine Learning Algorithms ◽  
Percentage Error ◽  
Price Forecasting ◽  
Price Prediction ◽  
Future Work ◽  
Gated Recurrent Unit

Cryptocurrency is a new sort of asset that has emerged as a result of the advancement of financial technology and it has created a big opportunity for researches. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Around the world, there are hundreds of cryptocurrencies that are used. This paper proposes three types of recurrent neural network (RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions depending on the mean absolute percentage error (MAPE). Results obtained from these models show that the gated recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models. Therefore, it can be considered the best algorithm. GRU presents the most accurate prediction for LTC with MAPE percentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The bi-LSTM algorithm presents the lowest prediction result compared with the other two algorithms as the MAPE percentages are: 5.990%, 6.85%, and 2.332% for BTC, ETH, and LTC, respectively. Overall, the prediction models in this paper represent accurate results close to the actual prices of cryptocurrencies. The importance of having these models is that they can have significant economic ramifications by helping investors and traders to pinpoint cryptocurrency sales and purchasing. As a plan for future work, a recommendation is made to investigate other factors that might affect the prices of cryptocurrency market such as social media, tweets, and trading volume.

Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

2019 ◽  
Vol 13 (2) ◽  
pp. 159-165
Author(s):  
Manik Sharma ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Genetic Algorithm ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Genetic Algorithm ◽  
Online Reviews ◽  
Middle Tier ◽  
Complete Set ◽  
Mining Model

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.

scholarly journals Classification of Fake Product Ratings Using a Timeline Based Approach

2017 ◽  
Vol 3 (2) ◽  
pp. 12 ◽  
Author(s):  
Neha Thomas ◽  
Susan Elias
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Optimal Point ◽  
Linear Classifiers ◽  
Wide Range ◽  
Text Content ◽  
Classification Tool ◽  
Fake Reviews ◽  
Product Ratings

 Abstract— Detection of fake review and reviewers is currently a challenging problem in cyber space. It is challenging primarily due to the dynamic nature of the methodology used to fake the review. There are several aspects to be considered when analyzing reviews to classify them effective into genuine and fake. Sentiment analysis, opinion mining and intend mining are fields of research that try to accomplish the goal through Natural Language Processing of the text content of the review.  In this paper, an approach that uses the review ratings evaluated along a timeline is presented. An Amazon dataset comprising of ratings indicated for a wide range of products was used for the analysis presented here. The analysis of the ratings was carried out for an electronic product over a period of six years.  The computed average rating helps to identify linear classifiers that define solution boundaries within the dataspace. This enables a product specific classification of review ratings and suitable recommendations can also be generated automatically. The paper explains a methodology to evaluate the average product ratings over time and presents the research outcomes using a novel classification tool. The proposed approach helps to determine the optimal point to distinguish between fake and genuine ratings for each product.    Index Terms: Fake reviews, Fake Ratings, Product Ratings, Online Shopping, Amazon Dataset.

scholarly journals Speech for Content Creation

2011 ◽  
Vol 3 (2) ◽  
pp. 35-49
Author(s):  
Joseph Polifroni ◽  
Imre Kiss ◽  
Stephanie Seneff
Keyword(s):  
Language Processing ◽  
Speech Processing ◽  
Opinion Mining ◽  
Prototype System ◽  
Dialogue Systems ◽  
Ancillary Data ◽  
Spoken Dialogue Systems ◽  
Content Creation ◽  
Sentiment Detection ◽  
Restaurant Reviews

This paper proposes a paradigm for using speech to interact with computers, one that complements and extends traditional spoken dialogue systems: speech for content creation. The literature in automatic speech recognition (ASR), natural language processing (NLP), sentiment detection, and opinion mining is surveyed to argue that the time has come to use mobile devices to create content on-the-fly. Recent work in user modelling and recommender systems is examined to support the claim that using speech in this way can result in a useful interface to uniquely personalizable data. A data collection effort recently undertaken to help build a prototype system for spoken restaurant reviews is discussed. This vision critically depends on mobile technology, for enabling the creation of the content and for providing ancillary data to make its processing more relevant to individual users. This type of system can be of use where only limited speech processing is possible.

Using Big Data Opinion Mining to Predict Rises and Falls in the Stock Price Index

2015 ◽  
pp. 30-42
Author(s):  
Yoosin Kim ◽  
Michelle Jeong ◽  
Seung Ryul Jeong
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Stock Price ◽  
Opinion Mining ◽  
Predictive Accuracy ◽  
Online News ◽  
Social Phenomena ◽  
Price Increases ◽  
Novel Approach ◽  
Investment Decision Making

In light of recent research that has begun to examine the link between textual “big data” and social phenomena such as stock price increases, this chapter takes a novel approach to treating news as big data by proposing the intelligent investment decision-making support model based on opinion mining. In an initial prototype experiment, the researchers first built a stock domain-specific sentiment dictionary via natural language processing of online news articles and calculated sentiment scores for the opinions extracted from those stories. In a separate main experiment, the researchers gathered 78,216 online news articles from two different media sources to not only make predictions of actual stock price increases but also to compare the predictive accuracy of articles from different media sources. The study found that opinions that are extracted from the news and treated with proper sentiment analysis can be effective in predicting changes in the stock market.

Modeling the co-citation dependence on semantic layers of co-cited documents

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maryam Yaghtin ◽  
Hajar Sotudeh ◽  
Alireza Nikseresht ◽  
Mahdieh Mirzabeigi
Keyword(s):  
Peer Review ◽  
Language Processing ◽  
Opinion Mining ◽  
Citation Network ◽  
Test Collection ◽  
Medical Subject Headings ◽  
Content Type ◽  
Citation Frequency ◽  
Mesh Terms ◽  
Citation Measures

PurposeCo-citation frequency, defined as the number of documents co-citing two articles, is considered as a quantitative, and thus, an efficient proxy of subject relatedness or prestige of the co-cited articles. Despite its quantitative nature, it is found effective in retrieving and evaluating documents, signifying its linkage with the related documents' contents. To better understand the dynamism of the citation network, the present study aims to investigate various content features giving rise to the measure.Design/methodology/approachThe present study examined the interaction of different co-citation features in explaining the co-citation frequency. The features include the co-cited works' similarities in their full-texts, Medical Subject Headings (MeSH) terms, co-citation proximity, opinions and co-citances. A test collection is built using the CITREC dataset. The data were analyzed using natural language processing (NLP) and opinion mining techniques. A linear model was developed to regress the objective and subjective content-based co-citation measures against the natural log of the co-citation frequency.FindingsThe dimensions of co-citation similarity, either subjective or objective, play significant roles in predicting co-citation frequency. The model can predict about half of the co-citation variance. The interaction of co-opinionatedness and non-co-opinionatedness is the strongest factor in the model.Originality/valueIt is the first study in revealing that both the objective and subjective similarities could significantly predict the co-citation frequency. The findings re-confirm the citation analysis assumption claiming the connection between the cognitive layers of cited documents and citation measures in general and the co-citation frequency in particular.Peer reviewThe peer review history for this article is available at https://publons.com/publon/10.1108/OIR-04-2020-0126.

An Opinion Mining Approach for Drug Reviews in Spanish

2021 ◽  
pp. 445-480
Author(s):  
Karina Castro-Pérez ◽  
José Luis Sánchez-Cervantes ◽  
María del Pilar Salas-Zárate ◽  
Maritza Bustos-López ◽  
Lisbeth Rodríguez-Mazahua
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Adverse Drug Events ◽  
Hybrid Approach ◽  
Unstructured Data ◽  
Degenerative Diseases ◽  
Analysis System ◽  
Essential Contribution ◽  
Processing Techniques ◽  
Manual Review

In recent years, the application of opinion mining has increased as a boom and growth of social media and blogs on the web, and these sources generate a large volume of unstructured data; therefore, a manual review is not feasible. For this reason, it has become necessary to apply web scraping and opinion mining techniques, two primary processes that help to obtain and summarize the data. Opinion mining, among its various areas of application, stands out for its essential contribution in the context of healthcare, especially for pharmacovigilance, because it allows finding adverse drug events omitted by the pharmaceutical companies. This chapter proposes a hybrid approach that uses semantics and machine learning for an opinion mining-analysis system by applying natural-language-processing techniques for the detection of drug polarity for chronic-degenerative diseases, available in blogs and specialized websites in the Spanish language.

Machine Learning Applications in Mega-Text Processing

2010 ◽  
pp. 325-347 ◽  
Author(s):  
Marina Sokolova ◽  
Stan Szpakowicz
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Opinion Mining ◽  
Personal Information ◽  
Text Processing ◽  
Machine Learning Techniques ◽  
Classification Problems ◽  
The Status ◽  
Machine Learning Applications ◽  
Applications Of Machine Learning

This chapter presents applications of machine learning techniques to problems in natural language processing that require work with very large amounts of text. Such problems came into focus after the Internet and other computer-based environments acquired the status of the prime medium for text delivery and exchange. In all cases which the authors discuss, an algorithm has ensured a meaningful result, be it the knowledge of consumer opinions, the protection of personal information or the selection of news reports. The chapter covers elements of opinion mining, news monitoring and privacy protection, and, in parallel, discusses text representation, feature selection, and word category and text classification problems. The applications presented here combine scientific interest and significant economic potential.

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