Tweepy Sentiment Analysis. Sentiment Analysis We finally compute the sentiment of each tweet For this we use NLTK’s SentimentIntensityAnalyzer object from the nltksentimentvader library VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rulebased sentiment analysis tool that is specifically attuned to sentiments expressed in social media.
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import tweepy # Authentication consumerKey = “Type your consumer key here” consumerSecret = “Type your consumer secret here” accessToken = “Type your accedd token here” accessTokenSecret = “Type your access token secret here” auth = tweepyOAuthHandler(consumerKey consumerSecret) authset_access_token(accessToken.
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Sentiment analysis uses Natural Language Processing (NLP) to make sense of human language and machine learning to automatically deliver accurate results Connect sentiment analysis tools directly to your social platforms so you can monitor your tweets as and when they come in 24/7 and get uptotheminute insights from your social mentions.
Simple Sentiment Analysis using Naive Bayes, Logistic
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Finally for our topic modelling analysis we used the Tweepy API to sample the top tweets from 2019–2020 Sentiment Analysis We started by performing a sentiment analysis of each corpus using the TextBlob library Sentiment analysis attempts to understand the subjective attributes of text such as mood We used TextBlob to determine a polarity and subjectivity.
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Headlines and Congressional Twitter: Sentiment and Topic
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