VADER Natural Language Processing in Market Sentiment Analysis

2020 ◽  
Author(s):  
Jonathan Seror
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Market Sentiment

scholarly journals A Systematic Study on Sentiment Analysis based on Text Mining and Deep learning for Predictions in Stock Market Trends Through Social and News Media Data

2021 ◽  
Vol 9 (10) ◽  
pp. 1589-1593
Author(s):  
Suvigya Jain
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Stock Market ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
News Media ◽  
Market Sentiment ◽  
Market Trends

Abstract: Stock Market has always been one of the most active fields of research, many companies and organizations have focused their research in trying to find better ways to predict market trends. The stock market has been the instrument to measure the performance of a company and many have tried to develop methods that reduce risk for the investors. Since, the implementation of concepts like Deep Learning and Natural Language Processing has been made possible due to modern computing there has been a revolution in forecasting market trends. Also, the democratization of knowledge related to companies made possible due to the internet has provided the stake holders a means to learn about assets they choose to invest in through news media and social media also stock trading has become easier due to apps like robin hood etc. Every company now a days has some kind of social media presence or is usually reported by news media. This presence can lead to the growth of the companies by creating positive sentiment and also many losses by creating negative sentiments due to some public events. Our goal in this paper is to study the influence of news media and social media on market trends using sentiment analysis. Keywords: Deep Learning, Natural Language Processing, Stock Market, Sentiment analysis

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

scholarly journals Coarse-Grained Sentiment Analysis Berbasis Natural Language Processing – Ulasan Hotel

2021 ◽  
Vol 10 (1) ◽  
pp. 41-48
Author(s):  
Warnia Nengsih ◽  
M. Mahrus Zein ◽  
Nazifa Hayati
Keyword(s):  
Natural Language Processing ◽  
Random Forest ◽  
Natural Language ◽  
Receiver Operating Characteristic ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Operating Characteristic ◽  
Coarse Grained ◽  
Receiver Operating

Sentiment analysis adalah metode untuk memperoleh data dari berbagai platform yang tersedia di internet. Kemajuan teknologi memungkinkan mesin untuk mengenali suatu istilah yang dianggap sebagai opini positif maupun sebaliknya. Data-data dan opini tersebut berperan penting sebagai umpan balik produk, layanan, dan topik lainnya. Tanpa perlu memperoleh opini secara langsung dari masyarakat, pihak penyedia telah mendapatkan evaluasi yang penting guna mengembangkan diri. Bisnis perhotelan merupakan bidang yang terkait dengan jasa memberikan layanan pada pelanggan. Indikator keberlangsungan bisnis ini juga bergantung pada umpan balik pelanggannya dan dijadikan sebagai acuan untuk pengambilan kebijakan strategis. Teknik sentiment analysis berbasis Natural Language Processing dapat mengatasi permasalahan tersebut. Pada makalah ini prediksi dilakukan menggunakan classifier Random Forest (RF), sementara untuk merangkum kualitas classifier, digunakan kurva Receiver Operating Characteristic (ROC). Kurva ROC berupa grafik yang baik untuk merangkum kualitas classifier. Semakin tinggi kurva berada di atas garis diagonal, semakin baik prediksinya, dengan nilai kurva ROC yang diperoleh sebesar 0,90. Terlihat hasil ulasan terhadap opini pelanggan terhadap jasa dan pelayanan yang diberikan oleh hotel untuk kategori positif lebih banyak daripada kategori negatif. Polaritas dari ulasan diperoleh 68% ulasan pelanggan berada pada area positif dan 32% berada pada area negatif.

scholarly journals Sentiment Analysis on Twitter Airline Data

2021 ◽  
Vol 9 (VI) ◽  
pp. 3767-3770
Author(s):  
Kirti Jain
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Task ◽  
Model Based ◽  
Sentiment Mining ◽  
General Opinion

Sentiment analysis, also known as sentiment mining, is a submachine learning task where we want to determine the overall sentiment of a particular document. With machine learning and natural language processing (NLP), we can extract the information of a text and try to classify it as positive, neutral, or negative according to its polarity. In this project, We are trying to classify Twitter tweets into positive, negative, and neutral sentiments by building a model based on probabilities. Twitter is a blogging website where people can quickly and spontaneously share their feelings by sending tweets limited to 140 characters. Because of its use of Twitter, it is a perfect source of data to get the latest general opinion on anything.

scholarly journals EMOSIS Sentiment Analysis on Tweets with Emotion and Intensity Level Recognition Considering Ending Punctuation Marks

2019 ◽  
Vol 8 (4) ◽  
pp. 10289-10293
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Emotion Recognition ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Significant Role ◽  
Language Model ◽  
Intensity Level ◽  
Processing Stage ◽  
Overall Performance

Sentiment Analysis is a tool used for determining the Polarity or Emotion of a Sentence. It is a field of Natural Language Processing which focuses on the study of opinions. In this study, the researchers solved one key challenge in Sentiment Analysis, which is to consider the Ending Punctuation Marks present in a sentence. Ending punctuation marks plays a significant role in Emotion Recognition and Intensity Level Recognition. The research made used of tweets expressing opinions about Philippine President Rodrigo Duterte. These downloaded tweets served as the inputs. It was initially subjected to pre-processing stage to be able to prepare the sentences for processing. A Language Model was created to serve as the classifier for determining the scores of the tweets. The scores give the polarity of the sentence. Accuracy is very important in sentiment analysis. To increase the chance of correctly identifying the polarity of the tweets, the input undergone Intensity Level Recognition which determines the intensifiers and negations within the sentences. The system was evaluated with overall performance of 80.27%.

scholarly journals Generalized approach to sentiment analysis of short text messages in natural language processing

2020 ◽  
pp. 2-14
Author(s):  
Evrenii Polyakov ◽  
Leonid Voskov ◽  
Pavel Abramov ◽  
Sergey Polyakov
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Full Range ◽  
Text Messages ◽  
Basic Solution ◽  
Short Text

Introduction: Sentiment analysis is a complex problem whose solution essentially depends on the context, field of study andamount of text data. Analysis of publications shows that the authors often do not use the full range of possible data transformationsand their combinations. Only a part of the transformations is used, limiting the ways to develop high-quality classification models.Purpose: Developing and exploring a generalized approach to building a model, which consists in sequentially passing throughthe stages of exploratory data analysis, obtaining a basic solution, vectorization, preprocessing, hyperparameter optimization, andmodeling. Results: Comparative experiments conducted using a generalized approach for classical machine learning and deeplearning algorithms in order to solve the problem of sentiment analysis of short text messages in natural language processinghave demonstrated that the classification quality grows from one stage to another. For classical algorithms, such an increasein quality was insignificant, but for deep learning, it was 8% on average at each stage. Additional studies have shown that theuse of automatic machine learning which uses classical classification algorithms is comparable in quality to manual modeldevelopment; however, it takes much longer. The use of transfer learning has a small but positive effect on the classificationquality. Practical relevance: The proposed sequential approach can significantly improve the quality of models under developmentin natural language processing problems.

scholarly journals Unmasking the conversation on masks: Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse

2020 ◽  
Author(s):  
Abraham Sanders ◽  
Rachael White ◽  
Lauren Severson ◽  
Rufeng Ma ◽  
Richard McQueen ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Public Attitudes ◽  
Online Activity ◽  
Health Crisis ◽  
Public Response ◽  
Health Community ◽  
High Level

In this exploratory study, we scrutinize a database of over 1 million tweets collected across the first five months of 2020 to draw conclusions about public attitudes towards the preventative measure of mask usage during the COVID-19 pandemic. In recent months, a body of literature has emerged to suggest the robustness of trends in online activity as proxies for the epidemiological and sociological impact of COVID-19. We employ natural language processing, clustering and sentiment analysis techniques to organize tweets relating to mask-wearing into high-level themes, then relay narratives for individual clusters through automatic text summarization. We find that topic clustering and visualization based on mask-related Twitter data offers revealing insights into societal perceptions of COVID-19 and techniques for its prevention. We observe that the volume and polarity of mask related tweets has greatly increased. Importantly, the analysis pipeline presented can be leveraged by the health community for the assessment of public response to health interventions in the ongoing global health crisis.

scholarly journals Sentiment of App with Word Vectors

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2156-2159
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Data ◽  
Vector Representations ◽  
Text Sentiment Analysis

Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment words extraction, polarity of sentiment words detection, and text sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. Vector representations has been used to compute various vector-based features and conduct systematically experiments to demonstrate their effectiveness. Using simple vector based features can achieve better results for text sentiment analysis of APP.

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