Analysing Tweet Sentiment with Azure -Part 2 (Python)

Building on the work I did yesterday using Azure to analyse Twitter tweet sentiment I have amended the function of the code so that it now marks the tweet as positive, neutral or negative. This is building up so that I can (hopefully) get my Twitter bot to retweet positive tweets about a subject of my choosing.

Full code available at: https://github.com/geektechdude/Tweet_Text_Analysis

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def get_tweets(search_term):

# this searches Twitter

results = twitter.cursor(twitter.search, q=search_term)

# this then pulls each individual tweet

for result in results:

# Tweet details that I may be interested in

tweet_id = result[‘id_str’]

tweet_text = result[‘text’]

tweeted_time = result[‘created_at’]

name = result[‘user’]

tweet_screen_name = name[‘screen_name’]

# Preparing the data to give to Azure

documents = {‘documents’ : [

{‘id’: tweet_id, ‘language’: ‘en’, ‘text’: tweet_text},

]}

# Sending the data to Azure

response = requests.post(sentiment_uri, headers=headers, json=documents)

# Getting response back from Azure

azure_response = response.json()

# Stripping the score out

azure_documents = azure_response[‘documents’]

azure_score = azure_documents[0][‘score’]

if azure_score >=0.6:

print(tweet_id,”\n”, tweet_text, “from \n”, tweet_screen_name)

print(azure_documents,”\n is postive”)

elif azure_score ==0.5:

print(tweet_id,”\n”, tweet_text, “from \n”, tweet_screen_name)

print(azure_documents,”\n is neutral”)

else:

print(tweet_id,”\n”, tweet_text, “from \n”, tweet_screen_name)

print(azure_documents,”\n is negative”)

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