Pre-Processing Tweets for Sentiment Analysis

Pre-Processing Tweets for Sentiment Analysis

Pre-Processing Tweets for Sentiment Analysis. We will start with preprocessing and cleaning of the raw text of the tweets.

When doing any Natural Language Processing (NLP) you will need to pre-process your data. In the following example I will be working with a Twitter dataset that is available from CrowdFlower and hosted on data.world.

Review the data

There are many things to consider when choosing how to preprocess your text data, but before you do that you will need to familiarize yourself with your data. This dataset is provided in a .csv file; I loaded it into a dataframe and proceeded to review it.

dataframe = pd.read_csv(data_file)
dataframe.head()

The first five rows of the dataframe

Just looking at the first five rows, I can notice several things:

  • In these five tweets the Twitter handles have been replaced with @mention

  • They all have the hashtag #SXSW or #sxsw

  • There is an html character reference for ampersand &

  • There are some abbreviations: hrs, Fri

  • There are some people’s real names, in this case public figures

This dataset contains about 8500 tweets, I won’t be able to review them all, but by reviewing segments of the dataset I was able to find other peculiarities to the data.

  • There are some url links, some with http or https, and some without

  • There are some url links that have been changed to a reference of {link}

  • There are some other html characters besides &

  • References to a video have been replaced with [video]

  • There were many non-english characters

* There were many emoticons

twitter python nlp sentiment-analysis data-science

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