Improving upon dual Twitter sentiment analysis with only one text summarization tool, my latest code uses 4 text summary tools. You can view it here. This version also saves the results to a text file in addition to displaying them on screen.

The main features of this code:

Uses the Twitter API to analyze up to thousands of tweets, depending on your search term, of course

Performs sentiment analysis using both TextBlob and VADER libraries; I only count a tweet as either positive, negative, or neutral if both libraries agree

Produces 1-sentence summaries of the tweets in each classification using LexRank, Luhn, LSA, and Stop Words (4 single-sentence summaries each for positive, negative, neutral, and unknown sentiments)

Some of the minor features of this code:

Reads up to 280 characters per tweet (the default when doing Twitter sentiment analysis is 140)

Displays the totals of each classification as a pie chart

Saves the totals, percentages, and summaries to a text file

The main problem with summarizing tweets, by the way, is retweets. Retweets are inherently repetitive, and you can see the effects of that if you generate even just a few summaries. A single summary, in fact, may have repetitive text due, presumably, to excessive retweeting.

One final note: I delete the working text files after I run the code; I am not storing tweets.