15 Tips for Using Machine Learning to Research Twitter Posts & Comments

15 Tips for Using Machine Learning to Research Twitter Posts & Comments

Twitter is not only one of the most popular social media platforms as outlined over at runrex.com, it is also one of the richest sources of data, which can be used for both academic research and business analysis. For brands, machine learning techniques can help them understand what people are saying about them, which allows them to understand their audience, stay on top of what is being said about them and their competitors, as well as discover new trends in their industry. This article will look to highlight 15 tips for using machine learning to research Twitter posts and comments.

• Understand what sentiment analysis is

One of the most important machine learning techniques you can apply on Twitter is sentiment analysis. As is outlined over at guttulus.com, sentiment analysis is the automated process of identifying and classifying subjective information in text data, with the most common type of sentiment analysis being polarity detection which involves classifying a statement as positive, negative, or neutral.

• Sentiment analysis and NLP

As is revealed in discussions on the same over at runrex.com, sentiment analysis uses Natural Language Processing (NLP) to process and make sense of human language, and machine learning to then automatically deliver accurate results.

• Come up with and understand the objective

An important tip when using machine learning techniques to research Twitter posts and comments, according to the gurus over at guttulus.com, is to make sure that you come up with and understand your objectives before you start working with the dataset. Come up with a problem statement beforehand, which you can then apply machine learning techniques to solve.

• Get your Twitter data

If you are to use machine learning to research Twitter posts and comments, then you will first need to get the data first. An important tip here, as highlighted over at runrex.com, is to make sure that you get the appropriate data for your project, one that is representative of what you are trying to find out since you will use the data to train your sentiment analysis model as well as to test how the model performs on Twitter data.

• Current vs historical tweets

When getting your Twitter data for your research, you will have to decide whether to go with current or historical tweets. A helpful tip when making this decision is to remember that current tweets are useful when looking to track hashtags or keywords in real-time, while historical tweets are useful when looking to compare sentiments over different periods.

• Platforms and tools to help you extract data from Twitter

There are several tools and platforms out there where you can go to for help extracting data from Twitter for your research, and this tip is going to highlight some of them. From discussions over at guttulus.com, examples of such platforms include Zapier, IFTT, MonkeyLearn, Export Tweet, Tweet Download among others.

• Data from the Twitter API

Another option available for you when looking for Twitter data is the Twitter API, which as explained over at runrex.com. lets you access and interact with public Twitter data. The only issue you may have with the Twitter API is that you may encounter limitations on how much data you can pull and how much access you will have.

• Streaming and Standard Search APIs

As is revealed in discussions over at guttulus.com, when extracting data from the Twitter API for your machine learning project, you should use the Twitter Streaming API to connect to Twitter data streams and collect tweets containing hashtags, brand mentions, and keywords, or collect tweets from specific users, while the Standard Search API will help you get historical tweets published up to 7 days ago.

• Tweepy

Another alternative to collecting data from Twitter is using Tweepy, and easy-to-use Python library for accessing the Twitter API, which is covered in detail over at runrex.com. Other Python libraries that may also come in handy include Twython and python-twitter among others.

• Clean your tweets

Before you apply any machine learning techniques to the tweets you have just gathered, it is important to process and clean your data to remove any ‘noise’ and ensure that you get accurate results. This involves tasks like removing emojis and other irrelevant information, special characters, duplicate tweets, short tweets with the accepted way to go about things here being to remove those that are shorter than 3 characters.

• Extracting features

As is explained over at guttulus.com, to analyze preprocessed data, it needs to be converted into features. Text features can be constructed using techniques such as Bag-of-Words and TF-IDF. The former is a method to represent text into numerical features, while the latter, although based on the frequency method, is different from the Bag-of-Words approach in that it takes into account the occurrence of a word not just in a single tweet (document) but in the entire corpus.

• Techniques to build models

Once you are done with the pre-modeling stages, you will need to build predictive models on your dataset. As is covered in detail over at runrex.com, you will have various options to choose from here from logistic regression to other machine learning algorithms like RandomForest, XGBoost, Support Vector Machine, and others.

• Train your model

The next stage is to train your sentiment analysis model, which can be achieved by tagging each of the tweets in your dataset as Positive, Negative, or Neutral based on the popularity of the opinion. As discussed over at guttulus.com, after tagging the first tweets, your model will start making its own predictions, and you can correct them if the answer given is not correct.

• Test your model

You should also not forget to test your model once you have trained it with a few examples. According to runrex.com, this is an important step as it will let you know just how accurate your model is and how well it is performing. Remember, the more training data you tag, the more accurate your model becomes.

• Visualization of results

You will also need data visualization tools to help you explain your sentiment analysis results, or the results of any other machine learning techniques, simply and effectively. Popular data visualization tools to consider include Google Data Studio, Tableau, and Looker.

These are just some of the things to consider when looking to use machine learning to research Twitter posts and comments, and you can uncover more insights on this wide topic by checking out the highly regarded runrex.com and guttulus.com.