Encoding for classifiers
You will have to use a mix of text processing and one hot encoding. Text column should not be treated as one-hot encoded since it will try to create one new variable for every unique sentence in the dataset, which will be a lot (and not very helpful from learning). Text vectorizer will summarize text column based on type of words/tokens that appear in it.
So you should use a text vectorizer for processing only the text column first. This will give you one data-frame (say A). This data-frame will have columns corresponding to tokens/words in the dataset. So if text vectorizer picks up 100 unique words then you will have a 1000x100 size data-frame. Note that these 100 columns have been generated only by text vectorizer.
For symbols and notes, you can use one-hot encoding, which will get you another data-frame (say B). Then you should join A &amp; B on common key to get your final data-frame if input. The common key here will be row ID (though read the following comment on aggregating data at user level).
It is not clear if the user name (Account) column unique in the data? If there are 1000 rows but only 400 users, that means there can be more than 1 row per user. In that case, you can consider aggregating the data at the user level (for text column, you can simply concat all strings for same user).