Study Data Analytics and New Technologies: the case of Sentiment Analysis Approach
The last few years, especially since the pandemic of Covid 19, have given way to an astronomical rise in how data is collected and interpreted for business intelligence and decision-making. As new digital technologies emerge to assist and enhance businesses, the Data Analysts will become more and more valuable leading to exceptional career progression and job security. Many Greek and Multinational companies are actively seeking to employ successful graduates with studies in Data Analytics and Data Science. As the demand for Data Analysts continues to grow across various industries, companies require skilled professionals who can analyze, interpret, and translate data into actionable insights. A graduate from this field, holds all the necessary technical and analytical skills for working in open high demand roles such as Data Analysts, Data Scientists, Business Analysts, CRM Consultants and more.
The Sentiment Analysis Approach of Big Data Analytics
Social Media networks are usually changed consumers’ behavior. Nowadays, more and more companies use the social media marketing in order to attract more customers. This modifies consumers’ attitudes and companies cannot detect these modifications due to the big volume and the diversity of the produced information. In this framework, the quality of services, marketing and maximization of sales will be enforced by considering the textual content that is generated by the Internet users. Sentiment analysis (subjective analysis, opinion mining, and appraisal extraction with some connections to affective computing are also alternative names) can approach methodologically the problem by identifying relevant information from the huge communication of stakeholders over the Internet. The opinionative data which are created continuously has resulted in the development of web opinion mining, as a concept in web intelligence focusing on extracting, analyzing and combining web data about user thoughts. The sentiment analysis is significant since the users’ opinions provide information of how people feel about a topic of interest and understand how this was acknowledged by the market. Sentiment classification analysis relies on three types of techniques, i.e., machine learning-based, lexicon-based and Hybrid techniques.
The NYC School of Informatics has the algorithm to succeed in this High Demand Industry. Study BSc (Hons) Computing (Data Analyst) and MSc of Data Analytics and Technologies in cooperation with the University of Bolton.