Mining and Analyzing LinkedIn Data

Plan of attack outlines extracting data from LinkedIn profiles with the Link View API and CSV files. Create three datasets: connections, invitations, and messages for data science and NLP.

Create a LinkedIn app to access profile data and prepare for data analysis, including verification, URL generation, and using the client IDs and secrets with Python.

demonstrates building a LinkedIn API workflow in Google Colab with Python, including client ID and secret handling, redirect URI, CSRF token, and an authorization URL to access basic profile data.

Learn how to authenticate with the LinkedIn API using the authorization code flow, obtain an access token, and fetch basic profile fields such as localized first name and last name.

Learn to obtain LinkedIn data by exporting CSV files for connections, invitations, and messages, and prepare these three datasets for data science techniques in upcoming lectures.

Load the connections dataset from csv, drop email, remove rows with missing first or last name, company, or position, and create a full name by joining first and last names.

Load the invitations dataset, examine its structure and shape (259 rows, five columns), and filter to incoming invitations to analyze who connects with your profile and their messages.

Finish loading and filtering the invitations dataset; map full names to company names from the connections dataset, producing an invitations file with sender companies saved as csv.

Generate fake names for LinkedIn connections using faker to preserve privacy, drop the first, last, and full names, and save the updated connections dataset with company, position, and date.

Load and preprocess the LinkedIn messages dataset, inspect fields like conversation id, sender, recipient, date, subject, and content, drop empty messages, and save the cleaned content for analysis.

Analyze LinkedIn data by mapping connections and invitations, building dynamic graphs of new requests, cluster people by companies and job titles, and visualize with Google Earth.

Explore the connections dataset and learn to compute connections by period, transform date formats, and visualize results with dynamic graphs grouped by date.

Group LinkedIn connections by month and by year, create a set month function, and add a connected month column to visualize monthly and yearly trends with a graph.

Analyze LinkedIn company data by counting unique companies and employee counts, using histograms and treemaps to visualize company attributes and positions.

Explore positions data by counting unique positions, visualize with histogram and three-map graphs, and group similar positions to improve clarity.

Discover how Levenshtein distance measures string similarity by counting insertions, deletions, and replacements to cluster job titles and compare LinkedIn profiles using Python's Levenshtein library.

learn n-gram similarity by building b-grams from strings, padding and counting shared grams, and implementing Python code to compare job titles like business intelligence analyst and data scientist.

Explore the Jaccard distance as a set-based similarity measure using intersection and union, with step-by-step implementation and practical text and computer vision examples.

Apply the Jack Card's distance to cluster job titles by string similarity. Test with a small pandas data frame containing English and Portuguese titles to group similar positions.

Cluster similar LinkedIn job positions by calculating string similarity and applying a mean distance threshold, building a dictionary of clusters and filtering single-element groups.

Apply the previous clustering code to the full LinkedIn connections dataset, recreate the positions variable, and explore clusters using order distance calculations, the anagram distance, and living standard distance.

Cluster similar positions into 331 groups from LinkedIn data and link each position to its users by building a cluster contacts dictionary and mapping names through connections.

Explore visualizing clusters in LinkedIn data by extracting common terms across positions, joining position lists, and displaying connected people for clearer cluster insights.

Export and analyze json data from LinkedIn, creating a json file of groups, clusters, and people, then load and prepare the data for visualizations like a dendrobium and a tree.

Visualize data with a dendrogram, adapting code for Google Colab, and govern cluster count and label length to reveal group structures like analysts, consultants, and founders.

Visualize a LinkedIn-like graph using alternative visualizations, highlighting clusters such as technical directors and founders. The code, based on mining this social web, is adapted to run in Google Colab.

Learn to infer LinkedIn contact locations by company name with the Google Location API, and set up a Google Cloud project, enable the API, and manage billing and API keys.

Explore the Google location API to geocode company names into latitudes and longitudes, validating locations across countries, with library installation and an API key workflow.

Extract latitude and longitude for all contacts in the datasets by looping through entries and using the Google API to fetch coordinates, saving results to a connections location file.

Visualize LinkedIn connections by plotting latitude and longitude on a basemap, creating a heat map and points to reveal geographic distribution and regional concentration.

Mine and analyze LinkedIn data by deriving country and city names from latitude and longitude with a reverse geocoder, enriching the dataset and generating graphs.

Generate and visualize graphs of contacts by country and city using histogram and map plots, filter to specific regions, and inspect positions and names for insights.

Explore clustering, a machine learning technique for grouping customers, texts, products, and social profiles by shared features, enabling market segmentation, topic discovery, and personalized recommendations.

Explore the k-means algorithm by clustering age and salary into three clusters using random initial centers, assigning points to the nearest center, and updating centroids as cluster means.

Apply k-means clustering to LinkedIn users by latitude and longitude, using Python and scikit-learn to identify regional patterns and visualize clusters in maps.

Learn to visualize LinkedIn contact data in Google Earth by generating KML files and applying k-means clustering to map 15 clusters across global regions.

Explore the invitations dataset by loading data into google colab, cleaning columns, adding geolocation attributes (latitudes, longitudes) and deriving country and city, and filtering to incoming invitations.

Fill in latitude and longitude, derive country and city for the invitations dataset using Google geocoding and reverse geocoding, then save the final data frame.

Analyze the invitations dataset to generate graphs, group data by country and city, and compare incoming invitations with the connections dataset to reveal which companies connect to your profile.

Plan of attack outlines analyzing LinkedIn text messages using natural language processing, covering preprocessing (lowercasing, removing links and HTML), stop words, spaCy named entity extraction, word clouds, and sentiment analysis.

Load the messages dataset, inspect the inbox (single-column text) between me and other users, identify duplicates, and prepare for pre-processing by dropping extra attributes and examining basic statistics.

Learn to preprocess texts for nlp by converting to lowercase, removing html codes and links. Apply stop-word removal and tokenization to produce clean, analyzable data.

Apply the preprocessing function to all messages, convert text to lowercase, and remove English stop words. Count words and filter texts to at least five words for reliable sentiment analysis.

Detect languages in text data and tailor sentiment analysis with language-specific classifiers. Use language packages for Portuguese, English, Spanish, and French and visualize results with word clouds.

Visualize the most frequent terms from LinkedIn data by concatenating texts and generating a word cloud, illustrating frequency with term size.

Explore named entity recognition to extract organizations, people, dates, and locations from LinkedIn data, with hands-on Python coding to filter GPE locations and visualize results.

Explore sentiment analysis on LinkedIn data using a sentiment density analyzer and Vader lexicon, computing polarity scores to classify messages as positive, neutral, or negative, and visualize results.

Recap three LinkedIn datasets: connections, invitations, and messages, and explore data science, graphs, a location API, and NLP techniques.

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