https://levelup.gitconnected.com/text-and-sentiment-analysis-of-whatsapp-messages-1eebc983a58
Positive Negative
"These are the most popular positive and negative words used throughout the chat. This is calculated using the Bing lexicon, which categorises words in a binary fashion into positive and negative categories"
Sentiment
Next, we look at the sentiment of each author over the course of the year. This is calculated using the AFINN lexicon which assigns words with a score that runs between -5 and 5, with negative scores indicating negative sentiment and positive scores indicating positive sentiment.
Post with more Graphs:
https://medium.com/analytics-vidhya/whatsapp-chat-analyze-visualize-68e4d30be729
https://towardsdatascience.com/a-comprehensive-data-analysis-on-a-whatsapp-group-chat-25d9535f3c65
https://towardsdatascience.com/a-comprehensive-data-analysis-on-a-whatsapp-group-chat-25d9535f3c65
Libaray for analysis
https://github.com/JBGruber/rwhatsapp
https://levelup.gitconnected.com/text-and-sentiment-analysis-of-whatsapp-messages-1eebc983a58
Positive Negative
"These are the most popular positive and negative words used throughout the chat. This is calculated using the Bing lexicon, which categorises words in a binary fashion into positive and negative categories"
Sentiment
Next, we look at the sentiment of each author over the course of the year. This is calculated using the AFINN lexicon which assigns words with a score that runs between -5 and 5, with negative scores indicating negative sentiment and positive scores indicating positive sentiment.
Post with more Graphs:
https://medium.com/analytics-vidhya/whatsapp-chat-analyze-visualize-68e4d30be729
https://towardsdatascience.com/a-comprehensive-data-analysis-on-a-whatsapp-group-chat-25d9535f3c65
https://towardsdatascience.com/a-comprehensive-data-analysis-on-a-whatsapp-group-chat-25d9535f3c65
Libaray for analysis
https://github.com/JBGruber/rwhatsapp