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Twitter Sentiment Analysis Using Machine Learning


Natural Language Processing (NLP) is a popular technology for research and data collection, and sentiment analysis is one of the most common sub-fields of NLP. Sentiment Analysis is the process of analyzing online pieces of writing to predict their emotional tone, i.e. whether a piece of information is positive, negative, or neutral. Tweets on specific topics can be analyzed this way to understand their sentiments.

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What is Twitter sentiment analysis?

Also known as ‘Opinion Mining’, the technology determines the opinions, attitudes, and emotions of the writer or subject. It ‘computationally’ understands a piece of writing or text by judging the polarity of content, i.e. whether the text sounds more positive, negative or neutral. It classifies the text into the right category by analyzing the words and averaging them out. For example, by picking up the positive and neutral words, it judges the overall sentiment of the sentence.

The process of sentiment extraction is completely automated. It is artificial intelligence that is analyzing the data, so thousands of text documents can be processed for sentiments and other features including topics, themes, etc. And where it might take hours for a team of people to manually do this task, Sentiment Analysis does it in seconds.

The technology comes into use in the social media space where it helps in finding out what people feel about certain topics, particularly in the context of Twitter or tweets. Twitter is one of the top social media platforms for information and interaction with brands and influential people across the world. Approximately 321 million active users send about 500 million tweets daily, therefore, this platform is a great channel for customer service and marketing strategy. Twitter allows the mining of data of any user through Twitter API or Tweepy. The data is tweets extracted from users, and Tweepy is the tool to access this data in a fairly simple way with Python.

How is Twitter sentiment analysis useful?

There are numerous applications where Twitter sentiment analysis comes into use including marketing, eCommerce, advertising, politics, and research. It helps enterprises get qualitative insights into people’s opinions about their products. Monitoring Twitter enables companies to know their audience, be on top of what is being said about their brand, discover new trends, and analyze the competition. But while analyzing Twitter data, just the quantitative metrics like the number of mentions or retweets are not enough, what matters is being able to grasp the effect of those mentions on the brand, whether they create a positive or negative effect. In the case of negative content going viral, social listening and monitoring of conversation/feedback become even more necessary as they can harm a brand’s reputation, leading up to an unexpected PR crisis. This is one of the reasons why Twitter sentiment analysis has become one of the important processes in social media marketing.

Uber used sentiment analysis and social media monitoring tools to find out whether users are liking the new version of their app. Social listening is used by them daily to understand what their users feel about the changes they implement. As soon as a modification is introduced they know whether it is being greeted with enthusiasm, or if it requires more work. The technology helped them understand whether their app was being received well.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

Different fields where Twitter sentiment analysis is used

a. Twitter sentiment analysis in Business

Strategies in marketing can be developed through Twitter sentiment analysis, as it helps in understanding customer feelings towards a brand or product. It explains why people respond to a certain product or campaign in a certain way. The analysis tool can identify posts conveying positive feedback as well as negative mentions or bad review about  a product.

b. Twitter sentiment analysis in Politics  

Political views can be tracked with the help of Twitter sentiment analysis model. It highlights inconsistencies between actions and statements at the government level and can also be used to predict election results. The sentiment analysis tool was used during the 2012 US presidential elections by the Obama administration to analyze the reception of policy announcements.

c. Twitter sentiment analysis in Public Actions

Social phenomenon can be tracked with the help of Twitter sentiment analysis. It could help identify dangerous situations or determine the general mood of an environment. It can help in crisis prevention by analyzing negative mentions in real-time, which allows reacting in the nick of time and nipping the problem in the bud.

How uses NLP for Twitter sentiment analysis is an end-to-end Machine Learning platform, which enables companies to attain useful information from unstructured data by using Computer vision, Natural Language Processing, and Data labeling. Skyl’s powerful Natural Language Processing platform lets enterprises work with texts, through systems such as sentiment analysis, entity analysis, and content classification. We help businesses automate processes through applications such as Twitter sentiment analysis, to enhance their decision-making skills and increase profits. Here are the steps with which Skyl used NLP for Twitter sentiment analysis:

Creating a project provides multiple templates in NLP and Computer Vision for a guided machine learning workflow. For this project, the Text Classification Multiclass template is chosen. A text classification multiclass template categorizes a piece of text into exactly one class from the given options of classes.

Creating a projectSelecting a template- Twitter sentiment analysis using machine learning

Designing the Dataset Schema then designed the schema of the dataset through a guided workflow. The dataset name, description and schema are designed as per the requirements of the project. The different categories were named as ‘positive’, ‘negative’ and ‘neutral’.

Designing the Dataset SchemaDataset schema- Twitter sentiment analysis using machine learning

Collecting data

The data is uploaded using the ‘CSV upload’ feature of Skyl. You can easily see the format in which the CSV file needs to be uploaded by downloading the schema from the button provided on the top right-hand side of the drag and drop window.

Collecting dataUploading CSV

Collecting data 2


Collecting data 2Data collection- Twitter sentiment analysis using machine learning

Labeling data provides the provision to create collaboration through a Form-based and mobile app. A Form-based collaboration job is created to label the data and add some collaborator emails.

Labeling dataData labeling - Twitter sentiment analysis using machine learning

Labeling data 2


Labeling data 3

It was easy to see how the job is progressing through the Overview tab along with the confusion matrix of the data.

Labeling data 4

Labeling data 5Labeling outlier trends- Twitter sentiment analysis using machine learning

Creating the Feature set

Once labeling was completed, created a feature set for Machine Learning training. A feature set is a subset of your dataset which is used as the input to your machine learning algorithm. We also provide a summary of the feature set you are creating so you can analyze if your feature set is properly balanced and there are no biases or bad data. After all, your machine learning model is only as good as the data it is being fed with.

Creating the FeaturesetCreating a feature set - Twitter sentiment analysis using machine learning

Model Training

We initiated the model training using Skyl’s suggested algorithms and parameters. It allows you to tune parameters like batch size, the number of epochs, learning rate, etc. as well as suggests the best possible optimized training parameters for the model training.  

Model TrainingModel Training - Twitter sentiment analysis using machine learning

Model Deployment

As soon as the training got over, a model was created which was listed under ‘Model Deployment’. The model achieved an accuracy of 92%. Training reports for the model were also generated for metrics like loss, accuracy, recall, precision, etc. allows one-click model deployment for your models, thus eliminating all the work required for setting up a model deployment pipeline.

Model DeploymentModel deployment - Twitter sentiment analysis using machine learning

Inference API

Inference APIs were generated once the model was deployed which could be hooked in any application and can be used for predictions. inference API is easy to use and available in all major programming languages for seamless integration.

Inference APIInference API - Twitter sentiment analysis using machine learning

Natural Language Processing (NLP) is a great way of researching data science and one of the most common applications of NLP is Twitter sentiment analysis. It applies Natural Language Processing to make automated conclusions about the text. The technology used in social media trend analysis and marketing, allows businesses to reach out to a broad audience and connect with customers directly. saves hours of manual data processing by automating business processes and turning tweets into actionable data.

Try out our risk-free trial to build and deploy your own Twitter sentiment analysis model using platform.