WEB AND SOCIAL MEDIA ANALYTICS B.Tech. IV Year I Sem JNTUH R-18

• How can analytics enhance supply chain efficiency and safety in a specific industry (e.g., healthcare, manufacturing)?

• Compare and contrast data-driven and intuition-based decision making in a business context.

• Describe the key capabilities and limitations of different types of decision support systems (e.g., DSS, EIS, ES).

• Discuss the challenges of implementing big data analytics solutions in organizations.

Unit II: Text Analytics and Text Mining

• Explain how NLP techniques can be used to identify entities, relations, and events within text data.

• Compare and contrast supervised and unsupervised machine learning approaches for text mining tasks.

• Describe the ethical considerations involved in collecting and analyzing large amounts of textual data.

• Discuss the potential applications of text mining in healthcare research, financial analysis, and legal document review.

Unit III: Sentiment Analysis

• Compare and contrast traditional sentiment analysis methods like lexicon-based and rule-based approaches with deep learning techniques.

• Explain how to handle sarcasm, irony, and other nuances of natural language in sentiment analysis.

• Discuss the potential biases and limitations of using sentiment analysis in social media monitoring.

• Propose a data-driven approach to improve customer satisfaction based on sentiment analysis of feedback data.

Unit IV: Web Analytics and Web Mining

• Explain how clickstream data can be used to identify user journeys and optimize website conversion rates.

• Discuss the role of A/B testing in website optimization and its limitations.

• Describe the ethical considerations involved in tracking user behavior on websites.

• Explain how web analytics can be used to personalize the user experience and improve customer engagement.

Unit V: Social Analytics and Social Network Analysis

• Compare and contrast different social media network visualization techniques and their insights.

• Explain how social network analysis can be used to identify influencers and communities within a social media platform.

• Discuss the challenges of measuring the return on investment (ROI) of social media marketing campaigns.

• Propose a strategy to monitor public perception of a brand using social media data and address potential reputational risks.