Sentiment analysis of financial news using unsupervised approach

Yadav, Anita; Jha, C. K.; Sharan, Aditi; Vaish, Vikrant

doi:10.1016/j.procs.2020.03.325

Cited by 40 publications

(13 citation statements)

References 14 publications

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“…4 In particular, we used sentiment analysis to detect the prevalent emotional tone associated to the discourse on gambling addiction on Twitter, aiming to improve the interpretation of results from the analysis based on the classification of information in topics. 67 We used the NRC Word-Emotion Association Lexicon dictionary, 68 including a list of English words and their associations with eight basic clusters of emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust, respectively). We used the probabilistic inference obtained through topic modeling to describe the semantic patterns underlying the text and to interpret the topics, and we supported the interpretation by performing a qualitative analysis of text when required.…”

Section: Methodsmentioning

confidence: 99%

Exploring the public’s perception of gambling addiction on Twitter during the COVID-19 pandemic: Topic modelling and sentiment analysis

Fino

Hanna-Khalil

Griffiths

2021

Journal of Addictive Diseases

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The present study explored the topics and sentiment associated with gambling addiction during the COVID-19 pandemic, using topic modeling and sentiment analysis on tweets in English posted between 17-24 th April 2020. The study was exploratory in nature, with its main objective consisting of inductively identifying topics embedded in user-generated content. We found that a five-topic model was the best in representing the data corpus, including: (i) the public's perception of gambling addiction amid the COVID-19 outbreak, (ii) risks and support available for those who stay at home, (iii) the users' interpretation of gambling addiction, (iv) forms of gambling during the pandemic, and (v) gambling advertising and impact on families. Sentiment analysis showed a prevalence of underlying fear, trust, sadness, and anger, across the corpus. Users viewed the pandemic as a driver of problematic gambling behaviors, possibly exposing unprepared individuals and communities to forms of online gambling, with potential long-term consequences and a significant impact on health systems. Despite the limitations of the study, we hypothesize that enhancing the presence of mental health operators and practitioners treating problem gambling on social media might positively impact public mental health and help prevent health services from being overwhelmed, in times when healthcare resources are limited.

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Section: Methodsmentioning

confidence: 99%

Exploring the public’s perception of gambling addiction on Twitter during the COVID-19 pandemic: Topic modelling and sentiment analysis

Fino

Hanna-Khalil

Griffiths

2021

Journal of Addictive Diseases

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“…Recall is the proportion of relevant examples correctly classified. The F1score is a harmonic average of precision and recall and is always closer to the smaller of the two-the higher the F1-score, the better the classifier's predictive power [28].…”

Section: Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis and Topic Modeling of Indonesian Public Conversation about COVID-19 Epidemics on Twitter

Habibi

Priadana

Ma’arif

2021

IJID

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The World Health Organization (WHO) declared the COVID-19 outbreak has resulted in more than six million confirmed cases and more than 371,000 deaths globally on June 1, 2020. The incident sparked a flood of scientific research to help society deal with the virus, both inside and outside the medical domain. Research related to public health analysis and public conversations about the spread of COVID-19 on social media is one of the highlights of researchers in the world. People can analyze information from social media as supporting data about public health. Analyzing public conversations will help the relevant authorities understand public opinion and information gaps between them and the public, helping them develop appropriate emergency response strategies to address existing problems in the community during the pandemic and provide information on the population's emotions in different contexts. However, research related to the analysis of public health and public conversations was so far conducted only through supervised analysis of textual data. In this study, we aim to analyze specifically the sentiment and topic modeling of Indonesian public conversations about the COVID-19 on Twitter using the NLP technique. We applied some methods to analyze the sentiment to obtain the best classification method. In this study, the topic modeling was carried out unsupervised using Latent Dirichlet Allocation (LDA). The results of this study reveal that the most frequently discussed topic related to the COVID-19 pandemic is economic issues.

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“…Accuracy and F1-Score are methods that are often used to see classifier performance [20]. The F1-Score determines the predictive power; the higher the F1-Score, the better [21]. Based on the results of the average confusion matrix in Table 1 and Table 2, it is found that the average accuracy value of the NBC method for the four sentiment classes is 58.89%.…”

Section: 1mentioning

confidence: 99%

Public Sentiments Analysis about Indonesian Social Insurance Administration Organization on Twitter

Rahmawati

Habibi

2020

IJID

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Insurance Administration Organization, which can be used by all people. However, this organization has received various criticisms from the public through social media, namely Twitter. This study aims to analyze public sentiment about the Indonesian Social Insurance Administration Organization on Twitter. The method used in this research is the Naive Bayes Classifier (NBC) method and uses the Support Vector Machine (SVM) method as a comparison. The amount of data used was 12,990 tweets with a data collection period from September 14, 2019 - February 18, 2020. The study compared the two classifier models built, namely the classifier model with two sentiment classes and four sentiment classes. The accuracy results show that the SVM method has a better accuracy value than the NBC method. SVM has an accuracy value of 63.60% and 82.77% for the two sentiment classes in the four sentiment classifier model. The tweet classification results show that the public's conversation about the Indonesian Social Insurance Administration Organization on Twitter has a negative polarity value tendency.

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Sentiment analysis of financial news using unsupervised approach

Cited by 40 publications

References 14 publications

Exploring the public’s perception of gambling addiction on Twitter during the COVID-19 pandemic: Topic modelling and sentiment analysis

Exploring the public’s perception of gambling addiction on Twitter during the COVID-19 pandemic: Topic modelling and sentiment analysis

Sentiment Analysis and Topic Modeling of Indonesian Public Conversation about COVID-19 Epidemics on Twitter

Public Sentiments Analysis about Indonesian Social Insurance Administration Organization on Twitter

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