Overview

Teaching: 30 min
Exercises: 20 min

Questions

• How can I analyze my tweets beyond what twarc offers?

• Is it possible to tell if tweets are expressing positive or negative feelings?

Objectives

• Sentiment analysis measures the ‘positivity’ or ‘negativity’ of language

Where We’ve Been: twarc Built-ins

We have looked at the hashtags in a dataset, the proportions of tweets to retweets, and the networks of users created by a tweet-stream. But what about content of the tweets? For that we need some textual analysis, and so far all we’ve done is count words.

Sentiment Analysis with TextBlob

TextBlob is a Python library that does all sorts of text processing, including sentiment analysis.

One type of sentiment analysis is to calculate the overall positivity or negativity of a text corpus. There are a variety of algorythms and scales. TextBlob’s default .sentiment function rates an input text as negative or positive on a scale of -1 to 1.

The sentiment function returns two numbers in the form of a named tuple:

Sentiment(polarity, subjectivity).

The polarity score is a float within the range [-1.0, 1.0]. Subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective (ie: does not express strong sentiment) and 1.0 is very subjective.

Before we can do any sentiment analysis, we need to download the linguistic datasets that TextBlob uses in its analyses:

!python -m textblob.download_corpora

We need to feed TextBlob something like plain text. Therefore, we need to create a text object from the tweets in our dataframe.

That column is named text, we pull that out of our dataframe and convert it to a list. Then we can convert the list into one long string.

TextBlob wants to get fed a string, so we will give it a string and convert that string into a blob. Then we send the blob through TextBlob’s sentiment method.

We can do this all in one cell, or one cell at a time to make sure we don’t have any mistakes.

# break tweets text column into a list, 
# then .join into one long string 
hashtagcats_list = ' '.join(hashtagcats_df['text'].tolist())
# turn the string into a blob
hashtagcats_blob = textblob.TextBlob(hashtagcats_list)
# get the sentiment
hashtagcats_blob.sentiment

Sentiment(polarity=0.2378257657037288, 
subjectivity=0.6328352645350159) ~~~ 

The overall sentiment of the language of Cat Twitter is rather positive. And the tweets tend to be subjective.

There’s a ton of other analyses and chunking functions in textblob. (Read the docs)[https://textblob.readthedocs.io/en/dev/quickstart.html].

Challenge: Anticipating Sentiment

Write Python code that outputs sentiment values for three of your dataframes. When you output the three values, arrange them in what you guess will be the least positive to most positive sentiment.

Please label your output. The syntax to pass output into the results cell is print("String in here ").

Remember, you need to pass TextBlob a string. A list will work:

your_data_list = ' '.join(your_data_df['text'].tolist())
your_data_blob = textblob.TextBlob(your_data_list)

Solution

I’m going to guess that all my datasets are going to be pretty subjective. Except for maybe ecodatascience.

My anticipated sentiment order:

• capitol riots
• gas prices
• the UCSB Library timeline
• ecodatascience timeline
• #catsofinstagram search
• fluffy #catsofinstagram search

This is one possible code block for our #catsofinstagram dataset:
hashtagcats_list = ' '.join(hashtagcats_df['text'].tolist())
hashtagcats_blob = textblob.TextBlob(hashtagcats_list)
print("Hashtag Cats of Instagram: ") 
hashtagcats_blob.sentiment

Can we do anything with the emojis?

Examine Emojis as a proxy measurement of qualitative emotional content. ie: you can visually see sentiment in emojis.

We still need code to isolate emojis #FIXME

They are emotional icons. #FIXME

Key Points

• TextBlob has a bunch of functions that we should learn