Hey! World this repo deals with sentiment analysis using python. Given some synthetic (fake, semi-randomly generated) twitter data in a csv file named project_twitter_data.csv which has the text of a tweet, the number of retweets of that tweet, and the number of replies to that tweet. Also given words that express positive sentiment and negative sentiment, in the files positive_words.txt and negative_words.txt. At first I build a sentiment classifier, which will detect how positive or negative each tweet is. After that I create a csv file, which contains columns for the Number of Retweets, Number of Replies, Positive Score (which is how many happy words are in the tweet), Negative Score (which is how many angry words are in the tweet), and the Net Score for each tweet. At the end, I upload the csv file to Google Sheets, and produce a graph of the Net Score vs Number of Retweets. If you are reading till now it means you already liked this project. So pull this code in your local repo and do some awesome stuff and let me know.
Sentiment analysis, also referred to as opinion mining, is an approach to natural language processing (NLP) that identifies the emotional tone behind a body of text. This is a popular way for organizations to determine and categorize opinions about a product, service, or idea. It involves the use of data mining, machine learning (ML) and artificial intelligence (AI) to mine text for sentiment and subjective information.