Using empythy to score likability based on sentiment analysis of recent tweets about a given person
Project description
Using empythy to score likability based on sentiment analysis of recent tweets about a given person
Purpose
To piggyback off of the empythy natural languare classifier package to analyze average sentiment of tweets related to a particular person to calculate a ‘likability score’ for that person. Useful in tracking sentiment changes across a certain period of time, i.e. the likability score of a celebrity before and after a concert.
Instructions
Open terminal. Make sure you have python3 and pip downloaded.
pip install likability
Create a csv file with the names of the people you’d like to analyze for likability. Name this file name.csv in the current directory.
Determine how many recent tweets you’d like to query for each person. This will be used in the script below as num_tweets.
Make sure you have Twitter API keys and access tokens. If you do not, go to [Twitter Apps](https://apps.twitter.com/), create an app, and find the required keys and tokens under Applications Settings -> Consumer Key (API Key) -> manage keys and access tokens.
Run Python 3 by typing python into the terminal.
Enter script below to run the LikabilityAnalyzer module.
from likability import LikabilityAnalyzer
filepath = 'name.csv'
num_tweets = 100
sentimentScore = LikabilityAnalyzer.analyzer(filepath,num_tweets)
When prompted, enter in your Twitter API keys. This will allow likability to access the Twitter API to query the tweets needed to complete the sentiment analysis.
Wait for script to run to completion. Please note, due to Twitter API Rate Limiting, querying more than 15 names will lead to longer wait times. Please allow 1 minute per name for lists greater than 15 names.
Upon completion, open the newly created Sentiment.csv in the current directory to access the likability scores for each person.
Possible Usage
Score top fantasy football players to see what the Twittersphere thinks about each player pre-draft
Instead of names of people, use product names to track customer sentiment in real-time
Solve the question: who is more likable, Justin Timberlake or Jimmy Fallon
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