Last week I wrote about doing a sentiment analysis of tweets about the President using Python's Sentiwordnet and Vader. Today, I switched to R and did a clustering and word cloud of the tweets about President Buhari.
- I imported all the necessary libraries.
- I connected to Twitter and created a search stream to gather tweets about Buhari
- I saved the results in a csv file with append set to true so I can keep piling up the search results from different time of the day. Then I removed punctuation and stopwords.
- I extracted the most frequently used words and created a word cloud from them. Lastly, I did a clustering of the words.
 |
| The require statements that were struck out were of libraries I didn't use but forgot take out before the screenshot |
Below is a screenshot of the scrapped tweets.
The article demonstrates a practical application of text mining by collecting Twitter data and applying clustering and word cloud analysis using R. It provides a simple yet effective workflow for preprocessing tweets, removing stop words, identifying frequently used terms, and visualizing public discussions, making it a useful introduction to social media analytics and natural language processing.
ReplyDeleteAnalyzing large collections of tweets requires techniques for organizing, processing, and extracting meaningful insights from unstructured textual data. Students interested in this field can explore Clustering Projects, where they can learn how unsupervised learning algorithms group similar data points and reveal hidden patterns in real-world datasets.
Since social media analytics often combines clustering with intelligent language understanding, working on Text Transformer Projects helps students build advanced NLP applications for sentiment analysis, topic discovery, and text classification using modern transformer-based architectures.
ReplyDeleteFor readers interested in exploring practical applications of large-scale data analysis and analytics-driven solutions, 15 Big Data Projects for Final Year Students showcases innovative project ideas that combine big data processing, machine learning, and intelligent decision-making across diverse domains.
ReplyDeleteAnalyzing social media conversations through clustering and word cloud techniques provides valuable insights into trending topics, public opinion, and frequently discussed keywords. By grouping similar tweets and visualizing dominant terms, researchers and analysts can better understand online discussions, identify emerging themes, and support data-driven decision-making in areas such as sentiment analysis and social media analytics.
ReplyDeleteLearners interested in creating meaningful charts, dashboards, and visual representations of data can enhance their skills through Data Visualization Training. Understanding data visualization techniques helps transform complex datasets into clear, actionable insights for analytics and reporting.
To further develop analytical skills for working with structured and unstructured datasets, Data Analysis Training introduces essential concepts such as data cleaning, exploratory data analysis, statistical techniques, and trend identification. These skills are widely used in business intelligence, social media analytics, machine learning, and data-driven decision-making across industries.
ReplyDelete