2024-10-26 –, CLASS #3
Language: English
In this talk, we will explore how to leverage Python for text analytics using data from Medium articles. This session will cover various Natural Language Processing (NLP) techniques to analyze and extract valuable insights from text data. Attendees will learn about data collection, preprocessing, sentiment analysis, and topic modeling, illustrated through practical examples and visualizations. Whether you are a content creator, data scientist, or Python enthusiast, this presentation will provide you with actionable methods and tools to enhance your text analytics capabilities.
This presentation will delve into the practical applications of Python in text analytics, focusing on data sourced from Medium articles. The talk will cover the following key areas:
Data Collection: Demonstrating web scraping techniques with BeautifulSoup and requests to gather Medium articles' data, including titles, content, and metadata.
Data Preprocessing: Using Pandas, nltk, and spaCy to clean and preprocess text data. This includes tokenization, stop words removal, and lemmatization.
Exploratory Data Analysis (EDA): Conducting an initial analysis to understand the dataset, including word frequency distribution and word clouds using Matplotlib and Seaborn.
Sentiment Analysis: Applying TextBlob and VADER for sentiment scoring of articles, visualizing sentiment trends over time, and comparing sentiments across different topics.
Topic Modeling: Utilizing Gensim and Latent Dirichlet Allocation (LDA) to uncover underlying topics in the text data, with visualizations using pyLDAvis.
Named Entity Recognition (NER): Identifying and categorizing entities with spaCy, extracting and visualizing entities mentioned in articles.
Visualization: Creating informative visualizations using Plotly to present findings effectively, including word clouds, sentiment trends, and topic distributions.
Case Study: Analyzing a selection of Medium articles to showcase the application of these techniques and present key findings such as common themes, sentiment trends, and notable entities.
The presentation aims to equip attendees with practical knowledge and tools to perform text analytics, making it valuable for content creators, data scientists, and Python enthusiasts alike
Data Professionals