463 research outputs found

    An Expert System Technique for Sentiment Analysis of Opinions

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    To help the users and the product owners it is quite necessary to extract aspects from the online reviews, their sentiment polarities, and associations between them. There is a great deal of work done in the field of sentiment analysis. Lexical and learning-based systems can be combined to separate the assessments from online opinions and reviews. In learning-based techniques, the Gaussian mixture model can be used for getting probabilistic results for polarities against aspects and naïve baize classifiers for the problem of spam comments which produced better and competitive results against previous techniques

    CLOUD-BASED MACHINE LEARNING AND SENTIMENT ANALYSIS

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    The role of a Data Scientist is becoming increasingly ubiquitous as companies and institutions see the need to gain additional insights and information from data to make better decisions to improve the quality-of-service delivery to customers. This thesis document contains three aspects of data science projects aimed at improving tools and techniques used in analyzing and evaluating data. The first research study involved the use of a standard cybersecurity dataset and cloud-based auto-machine learning algorithms were applied to detect vulnerabilities in the network traffic data. The performance of the algorithms was measured and compared using standard evaluation metrics. The second research study involved the use of text-mining social media, specifically Reddit. We mined up to 100,000 comments in multiple subreddits and tested for hate speech via a custom designed version of the Python Vader sentiment analysis package. Our work integrated standard sentiment analysis with Hatebase.org and we demonstrate our new method can better detect hate speech in social media. Following sentiment analysis and hate speech detection, in the third research project, we applied statistical techniques in evaluating the significant difference in text analytics, specifically the sentiment-categories for both lexicon-based software and cloud-based tools. We compared the three big cloud providers, AWS, Azure, and GCP with the standard python Vader sentiment analysis library. We utilized statistical analysis to determine a significant difference between the cloud platforms utilized as well as Vader and demonstrated that each platform is unique in its analysis scoring mechanism

    Sentiment Lexicon Construction Using SentiWordNet 3.0

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    Opinion mining and sentiment analysis have become popular in linguistic resource rich languages. Opinions for such analysis are drawn from many forms of freely available online/electronic sources, such as websites, blogs, news re-ports and product reviews. But attention received by less resourced languages is significantly less. This is because the success of any opinion mining algorithm depends on the availability of resources, such as special lexicon and WordNet type tools. In this research, we implemented a less complicated but an effective approach that could be used to classify comments in less resourced languages. We experimented the approach for use with Sinhala Language where no such opinion mining or sentiment analysis has been carried out until this day. Our algorithm gives significantly promising results for analyzing sentiments in Sinhala for the first time

    Stock Prediction Analyzing Investor Sentiments

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    We are going through a phase of data evolution where a major portion of the data from our daily lives is now been stored on social media platforms. In recent years, social media has become ubiquitous and important for social networking and content sharing. Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. In the financial sector, sentiments are also of paramount importance, and this dissertation mainly focuses on the effect of sentiments from investors [3] on the behavior of stocks. The dissertation work leverages social data from Twitter and seeks the sentiment of certain investors. Once the sentiment of the tweets is calculated using an advanced sentiment analyzer, it is used as an additional attribute to the other fundamental properties of the stock. This dissertation demonstrates how incorporating the sentiments improves forecasting accuracy of predicting stock valuation. In addition, various experimental analysis on regression based statistical models are considered which show statistical measures to consider for effectively predicting the closing price of the stock. The Efficient Market Hypothesis (EMH) states that stock market prices are largely driven by additional information and follow a random walk pattern [7, 8, 37, 39, 41]. Though this hypothesis is widely accepted by the research community as a central paradigm governing the markets in general, several people have attempted to extract patterns in the way stock markets behave and respond to external stimuli. We test a hypothesis based on the premise of behavioral economics, that the emotions and moods of individuals basically the sentiments affect their decision-making process, thus, leading to a direct correlation between ?public sentiment? and ?market sentiment? [42, 43, 44, 45]. We first select key investors from Twitter [27, 28] whose sentiments matter and do sentiment analysis on the tweets pertaining to stock related information. Once we retrieve the sentiment for every stock, we combine this information with the other fundamental information about stocks and build different regression-based prediction models to predict their closing price

    News sentiment to market impact and its feedback effect

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    Although market feedback on investor sentiment effect has been conceptually identified in the existing finance literature and investment strategies have been designed to explore this effect, there lacks systematic analysis in a quantified manner on such effect. Digitization of news articles and the advancement of computational intelligence applications have led to a growing influence of news sentiment over financial markets in recent years. News sentiment has often been used as a proxy for gauging investor sentiment and reflecting the aggregate confidence of the society toward future market. Previous studies have primarily focused on elucidating the unidirectional impact of news sentiment on market returns and not vice versa. In this study, we analyze more than 12 millions of news articles and document the presence of a significant feedback effect between news sentiment and market returns across the major indices in the US financial market. More specifically, we find that news sentiment exhibits a lag-5 effect on market returns and conversely market returns elicit consistent lag-1 effects on news sentiment. This aligns well with our intuition that news sentiment drives trading activity and investment decisions. In turn, heightened investment activity further stimulates involuntary responses, which manifest in the form of more news coverage and publications. The evidence presented highlights the strong correlation between news sentiment and market returns and demonstrates the benefits of advancing knowledge in data-driven modeling and its interaction with market movements

    Emotional Tendency Analysis of Twitter Data Streams

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    The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysi

    Extraction of opinionated profiles from comments on web news

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    Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

    A review of sentiment analysis research in Arabic language

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    Sentiment analysis is a task of natural language processing which has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Arabic sentiment analysis so far. In this paper, we carry out an in-depth qualitative study of the most important research works in this context by presenting limits and strengths of existing approaches. In particular, we survey both approaches that leverage machine translation or transfer learning to adapt English resources to Arabic and approaches that stem directly from the Arabic language

    Finetuning BERT and XLNet for Sentiment Analysis of Stock Market Tweets using Mixout and Dropout Regularization

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    Sentiment analysis is also known as Opinion mining or emotional mining which aims to identify the way in which sentiments are expressed in text and written data. Sentiment analysis combines different study areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is quickly becoming a key concern for businesses and organizations, especially as online commerce data is being used for analysis. Twitter is also becoming a popular microblogging and social networking platform today for information among people as they contribute their opinions, thoughts, and attitudes on social media platforms over the years. Because of the large database created by twitter stock market sentiment analysis has always been the subject of interest for various researchers, investors, and scientists due to its highly unpredictable nature. Sentiment analysis can be performed in different ways, but the focus of this study is to perform sentiment analysis using the transformer-based pre-trained models such as BERT(bi-directional Encoder Representations from Transformers) and XLNet which is a Generalised autoregressive model with fewer training instances using Mixout regularization as the traditional machine and deep learning models such as Random Forest, Naïve Bayes, Recurrent Neural Network (RNN), Long short-term memory (LSTM) because fails when given fewer training instances and it required intense feature engineering and processing of textual data. The objective of this research is to study and understand the performance of BERT and XLNet with fewer training instances using the Mixout regularization for stock market sentiment analysis. The proposed model resulted in improved performance in terms of accuracy, precision, recall and f1-score for both the BERT and XLNet models using mixout regularization when given adequate and under-sampled data
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