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Collecting Twitter Data: Converting Twitter JSON to CSV — Possible Errors

Part I: Introduction | Part II: Getting Started | Part III: Using a Python Stream Listener | Part IV: Storing Tweets in MongoDB | Part V: Twitter JSON to CSV — Errors [current page] | Part VI: Twitter JSON to CSV — ASCII | Part VII: Twitter JSON to CSV — UTF-8


Before diving into the problem of how to save tweets in a CSV file, let me say there are a 1,000 ways to do this and about 100 complications that arise depending which way you want to accomplish this. I will devote two posts which covers using both ASCII and UTF-8 encoding because many tweets contain characters beyond the normal Latin alphabet.

Let’s look at some of the issues with writing CSV from tweets.

  • Tweets are JSON and contain a massive amount of metadata. More than you probably want.
  • The JSON isn’t a flat structure; it has levels. [Direct contrast to a CSV file.]
  • The JSON files don’t all have the same elements.
  • There are many foreign languages and emoji used in tweets.
  • Tweets contain many different grammatical marks such as commas and quotation marks.

These issues aren’t incredibly daunting, but those unfamiliar will encounter frustrating errors.

Tweets are JSON and contain a massive amount of metadata. More than you probably want.

I’m always in favor of keeping as much data as possible, but tweets contain a massive amount of different metadata attributes. All of these are designed for the Twitter platform and for the associated client apps. Some items like the profile_background_image_url_https really don’t have much of an impact on any analysis. Choosing which attributes you want to keep will be critical before embarking on a process to parse the data into a CSV. There’s a lot to choose from: timestamp data, user data, retweet data, geocoding data, hashtag data and link data.

The JSON isn’t a flat structure; it has levels.

This issue is an extension of the previous issue, since tweet JSON data isn’t organized into a flat, spreadsheet-like structure. The created_at and text elements are located on the top level and are easy to access, but something as simple as the tweeter’s name and screen_name are located in the user nested object. Like everything else mentioned in this post, this isn’t a huge issue, but the structure of a tweet JSON file has to be considered when coding your program.

The JSON files don’t all have the same elements.

The final problem with JSON files is the fields aren’t necessarily present in every object. Many geo related attributes do not appear unless geotagging is enabled. This means if you write your program to look for geotagging data, it can throw a key error if those keys don’t exist in that specific tweet. To avoid this you have to account for the exception or use a method that already does that. I use the get() method to avoid these key errors in the CSV parser.

There are many foreign languages and emoji used in tweets.

I quickly addressed this issue in a few posts, and it’s one of the reasons why I like to store tweets in MongoDB. Tweets contain a lot of of [read: important] unicode characters. These are typically many foreign language characters and the ubiquitous emojis. This is important because the presence of UTF-8 unicode characters can and will cause encoding errors when parser a file or loading a file into Excel. Excel (at least the version on my computer) can’t handle these characters. Other tools like the built-in CSV writer in Python can’t handle unicode out of box. Being able to deal with these characters is critical to compatibility with other software as long as the integrity of your data.

This issue forces me to write two different parsers for examples. I have a CSV parser that outputs ASCII that imports well into Excel along with a UTF-8 version which allows you to natively save the characters and emojis in a human-readable CSV file.

Tweets contain many different grammatical marks such as commas and quotation marks.

This is a problem that I had when I first started working with Twitter data and tried to write my own parser — characters that are part of your text content sometimes get confused with the delimiters. In this case I’m talking about quotation marks (") and commas (,). Comma sseparate the values for each ‘cell’, hence the acronym CSV. If you tweet you’ve probably tweeted using one of these characters. I’ve stripped them out of the text previously to solve this problem, but that’s not a great solution. The way Excel handles this is to enclose any elements that contain commas with quotation marks then to use double quotation marks to signify an actual quotation mark and not enclosed text. This will be demonstrated in the UTF-8 parser since I made that from scratch.

Part I: Introduction | Part II: Getting Started | Part III: Using a Python Stream Listener | Part IV: Storing Tweets in MongoDB | Part V: Twitter JSON to CSV — Errors [current page] | Part VI: Twitter JSON to CSV — ASCII | Part VII: Twitter JSON to CSV — UTF-8

emoji header

The Most Popular Emoji Characters on Twitter

On Twitter, about 10% of general-topic tweets contain emoji characters, the tiny icons and emoticons, which are starting to get more attention when analyzing tweets, Facebook messages, or text messages. An emoji [] can capture an emotion or completely change the meaning of the written text. Before exploring how different emojis are used and what they mean to people, I wanted to get an idea of how prevalent they are and which ones are the most popular on Twitter.


Changes Meaning:

How I Did This

I collected tweets using a sampled stream from Twitter. In order to get a general representative sample of tweets, I tracked five popular, basic words: ‘the’, ‘and’, ‘to’, ‘you’, and ‘it’. These words are good search words, since there aren’t many sentences or thoughts that don’t use them. A Python script was used to find and count all the the emojis present in a collection of over 100,000 tweets. To avoid skewing due to a popular celebrity or viral tweet, I removed any retweets which were obvious retweets, and not retweets which function more like mentions.


Emoji Use on Twitter

In the general collection of tweets, I found that 10.23% of tweets contained at least one emoji. So there isn’t an overwhelming number of tweets which contain an emoji, but 10% of Twitter content is a significant portion. The ‘Emoji Selection’ graph shows the percentage of tweets containing that particular emoji out of the tweets that HAD an emoji in it. The most popular emoji by and far was the ‘tears of joy’ emoji followed by the ‘loudly crying’ emoji . Heart-related emoji [the ones I thought would prove most popular] was third and fourth.

Emoji Selection on Twitter

Since I only collected these over the course of a day and not over several weeks or months, I would be hesitant to think these results would hold up over time. An event or seasonality can trigger a cascade of people using a certain emoji. For example, the Christmas tree emoji was popular being present in 2.16% of tweets that included emojis; this would be expected to get larger as we get closer to Christmas and smaller after Christmas. Another interesting find is that the emoji ranks high. My pure conjecture is that this emoji’s high use rate is due to protests in Ferguson and around the country. To confirm this I would need a sample of tweets from before the grand jury announcement or track the use as time passes.

Further analysis could utilize emoji groups or clusters. Emojis with similar meanings would not necessarily produce a high number if people spread their selection over 5 emoji instead of one. I plan to update this and expand on this as time passes and I’m able to collect more data.


In order to avoid any conflicts with ASCII conversions that some Python or R packages do on Twitter data, I stored tweets from the Twitter Streaming API directly into a MongoDB database, which encodes strings in UTF-8. Since tweets come from the API as a JSON object, they can be naturally stored in the document-orientated database with each metadata field in the tweet being accessible without parsing the entire tweet into a data frame or SQL database. Retweets were removed by finding any tweets with ‘RT’ in the first two characters of the text entry. This is how Twitter represents automatic retweets in JSON format.

Also since I collected 103,416 tweets the margin of error for any of the proportions given are well below 1%. Events within the social network would definitely outweigh any margin of error.

Emoji, UTF-8, and Python

I have updated [better] code that allows for easy counting of emoji’s in string objects in Python, it can be found on my GitHub. I have a two counting classes in a mini-package loaded there.

Emoji [], those ubiquitous emoticons that popped up when iPhone users found them in 2011 with iOS 5 are a different set of characters aside from the traditional alphanumeric and punctuation characters. These are essentially another alphabet, and this concept will be useful when using the emoji in Python. Emoji are NOT a font like Wingdings from Windows95, they are unique characters with no corresponding letter or symbol representation. If you have a document or webpage that has the Wingding font, you can simply change the font to a typical Latin font to see the normal characters the Wingding font represents.

Technical Background

Without getting into the technical encoding problems, emoji are defined in Unicode and UTF-8, which can represent just about a million characters. A lot of applications or software packages default to ASCII, which only encodes the typical 128 characters. Some Python IDEs, csv writing packages, or parsing software default to or translate to ASCII, so they don’t necessarily handle the emoji characters properly.

I wrote a Python script [or this Python ‘package’] that takes tweets that are stored in a MongoDB database (more on that later) and counts the number of different emoji in the tweet corpus. To make sure Python plays nice with the emojis, first I loaded in the data by making sure I had UTF-8 encoding specified otherwise you’ll get this encoding error:

UnicodeDecodeError: 'ascii' codec can't decode byte 0xf0 in position 0: ordinal not in range(128)

I loaded an emoji key I made using all the emoji’s in Apple’s implementation by loading this code into a Panda’s data frame:

emoji_key = pd.read_csv('emoji_table.txt', encoding='utf-8', index_col=0)

If Python loads you data in correctly with UTF-8 encoding, each emoji will be treated as separate unique character, so string function and regular expressions can be used to find the emoji’s in other strings such as Twitter text. In some IDEs emoji’s don’t display [Canopy] or don’t display well [PyCharm]. I remedied the invisible/messy emoji’s by running the script in Mac OS X’s terminal application, which displays emoji . Python can also produce an ASCII compliant string by using a unicode escape encoding:


The escape encoded string will display something like this:


All IDEs will display the ASCII string. You would need to decode it from the unicode escape to get it back into a unicode object. Ultimately I had a Pandas data frame containing unicode objects. To make sure the correct encoding was used on the output text file, I used the following code:

 with open('emoji_out.csv', 'w') as f: 
    emoji_count.to_csv(f, sep=',', index = False, encoding='utf-8')  

Emoji Counter Class

I made an emoji counter class in Python to simplify the process of counting and aggregating emoji counts. The code [socialmediaparse] is on my GitHub along with the necessary emoji data file, so it can load the key when the instance is created. Using the package, you can repeatedly call the add_emoji_count() method to change the internal count for each emoji. The results can be retrieved using the .dict, dict_total, and .baskets attributes of the instance. I wrote this because it organizes and simplifies the analysis for any social media or emoji application. Separate emoji dictionary counter objects can be created for different sets of tweets that someone would want to analyze.

import socialmediaparse as smp #loads the package

counter = smp.EmojiDict() #initializes the EmojiDict class

#goes through list of unicode objects calling the add_emoji_count method for each string
#the method keeps track of the emoji count in the attributes of the instance
for unicode_string in collection:

#output of the instance
print counter.dict_total #dict of the absolute total count of the emojis in corpus
print counter.dict       #dict of the count of strings with the emoji in corpus
print counter.baskets    #list of lists, emoji in each string.  one list for each string.

counter.create_csv(file='emoji_out.csv')  #method for creating csv


MongoDB was used for this project because the data stores the JSON files very well, not needing a parser or a csv writer. It also has the advantage of natively storing strings in UTF-8. If I used R’s StreamR csv parser, there would be many encoding errors and virtually no emoji’s present in the data. There might be possible work arounds, but MongoDB was the easiest way I’ve found to work with Twitter JSON, UTF-8 encoded data.