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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. [ABSTRACT FROM AUTHOR]