Posttherapy Follow‐up and First Intervention

Cordeiro, Ernesto; Anastasiadis, Anastasios; Westendarp, Matías; Rosette, Jean J.M.C.H. de la; Reijke, Theo M. de

doi:10.1002/9781118347379.ch11

Cited by 1 publication

(1 citation statement)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of supervised learning, the most prevalent approaches are the naïve Bayesian classifier or Support Vector Machine (SVM) models (Cortes and Vapnik, 1995), maximum Entropy (Berger, Della Pietra and Della Pietra, 1996) and logistic regression. Read proposes a research in which SVM and naïve Bayesian classifiers are used across a collection of onedimensional representations that indicate the binary existence of vocabulary terms in the input texts (Cordeiro et al, 2014). According to Denecke (2008), input representations are derived using the SentiWordNet sentiment lexicon (Esuli and Sebastiani, 2006) word-level sentiment scores (Denecke, 2008).…”

Section: Sentiment Analysismentioning

confidence: 99%

Using Data Mining in the Sentiment Analysis Process on the Financial Market

Cristescu

Nerișanu

Mara

2022

Journal of Social and Economic Statistics

View full text Add to dashboard Cite

Sentiment analysis refers to the analysis of human opinions and sentiments that are expressed in written text, being also a part of the Natural Language Processing (NLP) tasks. Sentiment analysis can be applied in different domains, especially in the corporate marketing and sales, the healthcare system or the financial market analysis. In this paper we aim to highlight how data mining is able to extract the sentiment score from a financial platform that shows the major headlines regarding stocks, in order to highlight the publications’ positive or negative opinion over a stock. In order to gain the sentiment score we have scraped text data from the platform Finviz from which the polarity of the opinion may be extracted. We have also used Valence Aware Dictionary for Sentiment Reasoning (VADER), by running a Python script using the BeautifulSoup library. After that we have used Pandas (Python Data Analysis Library) to analyse and obtain a sentiment score on the article headlines. Results show that the script is able to generate the sentiment score for various selected stocks, while also showing graphical diagrams for the past and future trend of the stock, in terms of overall opinion on the stock performance.

show abstract