Negation detection in sentiment analysis
Here are a few suggestions on how to improve the code:
State the intent
What is the purpose of the function? What is the input, what is the output? In human words, what does the algorithm do? A docstring answering those basic questions would be helpful. A series of concise unit tests would be great.
I ran the function to have a look at the output:
&gt;&gt;&gt; text = &quot;The weather is not good.&quot;
&gt;&gt;&gt; result = negate_sequence(self=None, text=text)
&gt;&gt;&gt; print(result)

['the', 'weather', 'the weather', 'is', 'weather is', 'the weather is', 'not', 'is not',
'weather is not', 'not_good', 'not not_good', 'is not not_good']

This doesn't ring a bell with me, so I stopped trying to understand the purpose.
Avoid stateful loops
Iteration i is coupled to iteration i-1 by the negation variable, this makes the logic hard to understand and error prone. If you work on bigrams/trigrams, I'd create a list of bigrams/trigrams and iterate over the tuples. This decouples the iterations.
Breakup long functions
This has almost endless benefits, as a starting point see this article. Some possibilities:

Have the text broken up into all lowercase and without punctuation by extract_words(text)
Have the list of trigrams created by make_trigrams(words)
Inspect the trigrams by process(trigrams)
If needed, have some kind of aggregate(results)

Once this is done, I guess we are much better to prepared to identify bugs and to further improve functionality.