JNTU MCA 2020 SOCIAL MEDIA INTELLIGENCE Previous Question Paper (October/ November - 2020 - R15)

Social media monitoring is the systematic process of tracking, collecting, and analyzing conversations and mentions across various social media platforms. It involves using specialized tools to listen to what is being said about a brand, product, competitor, or specific industry topic in real-time.

The primary objectives include:

• Brand Reputation Management: Identifying positive or negative sentiment to address customer concerns promptly.
• Customer Engagement: Discovering opportunities to interact directly with the audience.
• Competitive Intelligence: Monitoring competitor activities and market positioning.
• Crisis Detection: Identifying potential PR issues before they escalate.

Text mining, also known as text data mining, is the process of deriving high-quality information from unstructured text. It involves transforming raw text into a structured format to identify patterns, trends, and relationships using natural language processing (NLP) and analytical methods.

The key stages of the text mining process are:

• Text Preprocessing: Cleaning the data by removing noise, such as HTML tags, punctuation, and stop words, followed by tokenization, stemming, or lemmatization to normalize the text.
• Feature Extraction: Converting text into numerical representations, such as Bag-of-Words (BoW) or Term Frequency-Inverse Document Frequency (TF-IDF), which algorithms can process.
• Pattern Discovery: Applying techniques like classification (categorizing text into predefined labels), clustering (grouping similar documents), and sentiment analysis (determining the emotional tone).
• Evaluation and Interpretation: Analyzing the extracted patterns to gain actionable insights, such as identifying emerging topics or customer preferences.

Text mining is essential in social media intelligence for processing massive volumes of user-generated content, enabling organizations to automate the analysis of reviews, comments, and social posts at scale.