Rule-Based Sentiment Analysis for Financial News

Tan, Li Im; Phang, Wai San; Chin, Kim On; Anthony, Patricia

doi:10.1109/smc.2015.283

Cited by 29 publications

(12 citation statements)

References 1 publication

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“…Supervised Learning is a type of learning with labeled datasets.Such datasets are passed to supervised learning models to predict further [6,7,10].…”

Section: B a Supervised Learningmentioning

confidence: 99%

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Assessing Security of Twitter by Sentimental Analysis using Deep Learning

Palherkar¹,

Singh²,

Sharma³

et al. 2023

IJRASET

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In order to make an approximation that is as accurate as feasible, sentiment analysis or opinion analysis is essential. Given that thoughtfully designed and carried out sentiment analysis can produce better and more accurate projections in both politics and business, this is a very essential component. At its most fundamental, feeling Analysis is based on the views that users and individuals share or express.There is a vast amount of material posted and exchanged over the internet every day by the users on various platforms. Understanding how different products, services, political figures, businesses, governments, and other entities are considered and perceived could be gained by being able to find the pattern in such data. This can handle a range of challenges, including being more reliable.Although we have numerous methods for sentiment analysis, a successful plan for regularly extracting and producing reliable sentiment analysis needs to be constructed. Despite the fact that machine learning algorithms have significantly improved—Naive Bayes, Support Vector Machine, and Maximum Entropy are the three that stand out as being particularly popular techniques for analysis, including good and bad views,is very much under study.

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“…Supervised Learning is a type of learning with labeled datasets.Such datasets are passed to supervised learning models to predict further [6,7,10].…”

Section: B a Supervised Learningmentioning

confidence: 99%

“…The technique continues for N number of records and when they are linked with the lead nodes that are making use of classification, depending on whether one or more words are present or absent. [10].…”

Section: Decision Tree Classifiersmentioning

confidence: 99%

Assessing Security of Twitter by Sentimental Analysis using Deep Learning

Palherkar¹,

Singh²,

Sharma³

et al. 2023

IJRASET

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“…When classifying text, we can classify at the word, sentence, or document level. There are different classification methods, including rule-based [12][13][14], support vector machine [15,16], random forest [17], Naive Bayes [18,19], embedding-based [20,21], as well as sentiment analysis using neural networks [22][23][24][25]. Additionally, we may classify sentiment by using unsupervised methods such as methods using rule-based unsupervised sentiment analysis [26], embeddings such as Word2Vec and Doc2Vec [27], and lexical resources for sentiment analysis [28].…”

Section: Sentiment Analysismentioning

confidence: 99%

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis (Preprint)

Monselise¹,

Chang²,

Ferreira³

et al. 2021

Preprint

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BACKGROUND As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. OBJECTIVE The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media. METHODS To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity. RESULTS After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy. CONCLUSIONS This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.

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“…To help the organizations to improve their decision making and improve customer satisfaction Zaw and Tandayya (2018) applied rulebased algorithm called Contrast Rule-Based sentiment analysis to classify customer reviews automatically. Another rule-based algorithm is proposed by Tan et al (2015) for classifying financial news articles. According to the research, initial stage was to determine the sentiment of each single sentence in the given financial news.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Polarity Detection in Azerbaijani Social News Articles

Mammadli¹,

Huseynov²,

Alkaramov³

et al. 2019

Proceedings - Natural Language Processing in a Deep Learning World

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Text classification field of natural language processing has been experiencing remarkable growth in recent years. Especially, sentiment analysis has received a considerable attention from both industry and research community. However, only a few research examples exist for Azerbaijani language. The main objective of this research is to apply various machine learning algorithms for determining the sentiment of news articles in Azerbaijani language. Approximately, 30.000 social news articles have been collected from online news sites and labeled manually as negative or positive according to their sentiment categories. Initially, text preprocessing was implemented to data in order to eliminate the noise. Secondly, to convert text to a more machine-readable form, BOW (bag of words) model has been applied. More specifically, two methodologies of BOW model, which are tf-idf and frequency-based model have been used as vectorization methods. Additionally, SVM, Random Forest, and Naive Bayes algorithms have been applied as the classification algorithms, and their combinations with two vectorization approaches have been tested and analyzed. Experimental results indicate that SVM outperforms other classification algorithms.

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Rule-Based Sentiment Analysis for Financial News

Cited by 29 publications

References 1 publication

Assessing Security of Twitter by Sentimental Analysis using Deep Learning

Assessing Security of Twitter by Sentimental Analysis using Deep Learning

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis (Preprint)

Sentiment Polarity Detection in Azerbaijani Social News Articles

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