Social media and topic modeling: how to analyze posts in practice

There is a substantial amount of data generated on the internet every second — posts, comments, photos, and videos. These different data types mean that there is a lot of ground to cover, so let’s focus on one — text.

All social conversations are based on written words — tweets, Facebook posts, comments, online reviews, and so on. Being a social media marketer, a Facebook group/profile moderator, or trying to promote your business on social media requires you to know how your audience reacts to the content you are uploading. One way is to read it all, mark hateful comments, divide them into similar topic groups, calculate statistics and… lose a big chunk of your time just to see that there are thousands of new comments to add to your calculations. Fortunately, there is another solution to this problem — machine learning. From this text you will learn:

Before jumping to the analyses, it is really important to understand why social media texts are so unique:

These features make social media a whole different source of information and demand special attention while running an analysis using machine learning. In contrast, most open-source machine learning solutions are based on long, formal text, like Wikipedia articles and other website posts. As a result, these models perform badly on social media data, because they don’t understand additional forms of expression included. This problem is called domain shift and is a typical NLP problem. Different data also require customised data preparation methods called preprocessing. The step consists of cleaning text from invaluable tokens like URLs or mentions and conversion to machine readable format (more about how we do it in Sotrender). This is why it is crucial to use tools created especially for your data source to get the best results.

Machine learning for text analysis (Natural Language Processing) is a vast field with lots of different model types that can gain insight into your data. One of the areas that can answer the question “what are the topics of given pieces of texts?” is topic modeling. These models help with understanding what people are talking about in general. It does not require any specially prepared data set with predefined topics. It can find topics which are patterns hidden within the data on its own without supervision and help — which makes it an unsupervised machine learning method. This means that it is easy to build a model for each individual problem.

There are lots of different algorithms that can be used for this task, but the most common and widely used is LDA (Latent Dirichlet Allocation). It’s based on word frequencies and topics distribution in texts. To put it simply, this method counts words in a given data set and groups them based on their co-occurrence into topics. Then the percentage distribution of topics in each document is calculated. As a result this method assumes that each text is a mixture of topics which works great with long documents where every paragraph relates to a different matter.

That’s why social media texts need a different procedure. One of the new algorithms is GSDMM (Gibbs sampling algorithm for a Dirichlet Mixture Model). What makes this one so different?:

Students are told to write down some movie titles they liked within 2 minutes. Most students are able to list 3–5 movies with this time frame (it corresponds to a limited number of words for social media texts). Then they are randomly assigned to a group. The last step is for every student to pick a different table with two rules in mind:

This last step is repeated multiple times. First rule that favours bigger groups is crucial to ensure that groups are not excessively fragmented. Due to the limited number of movie titles (words) for each student (text), each group (topic) is bound to have members with different movies in their lists but from the same genre.