Sentiment analysis allows you to automatically extract opinions from text.
For businesses, this provides a one-of-a-kind opportunity to understand how their customers feel about products or services. By running sentiment analysis on text data such as social media posts, emails, chats, surveys, and reviews, you can capture the subjective information. That way, you can spot customers complaining about poor customer service on Twitter, or those praising specific features of an app on review sites.
On the contrary to popular belief, you don’t need to be a tech expert to get started with sentiment analysis. Even though building a model from scratch does require programming skills and machine learning knowledge, there’s an increasing number of sentiment analysis tools that make automating tasks with sentiment analysis a simple and intuitive task.
In this guide, we’ll review the basics of sentiment analysis and delve into online tools. We’ll present some of the advantages of using them, and list some of the best tools you can find online. Finally, we’ll provide a step-by-step tutorial so that you can create a customized sentiment analysis classifier using MonkeyLearn.
- What is sentiment analysis?
- Why is it important?
- What are sentiment analysis tools?
- How to create a custom sentiment analysis model?
Let’s dive right in!
What is Sentiment Analysis?
Also known as opinion mining, sentiment analysis is the automated process of identifying the feelings that underlie a text, and classifying the text as positive, negative, or neutral based. For example,
“I like how easy it is to get started. The interface is a delight to use.” → Positive.
“There is really nothing bad about it, but the design could be improved”. → Neutral
“Absolutely abysmal customer service. Totally disappointed!” → Negative.
Besides capturing the nuance of the opinion (polarity), you can also extract the topic of the opinion by combining a sentiment analysis with topic analysis, otherwise known as aspect-based sentiment analysis. In this example: “Slack has a very nice and clean interface”, the opinion positively refers to a specific aspect of Slack’s platform: its interface.
Sentiment analysis is one of the most exciting applications of Natural Language Processing (NLP), a branch of artificial intelligence that studies how machines process human language. Sentiment analysis combines this knowledge alongside statistics and machine learning techniques to create models that can make predictions based on previous examples.
Why is Sentiment Analysis Important?
Every day, people conduct 5 billion searches, send 294 billion emails, and post 500 million tweets. Now, imagine the amount of data companies must have to deal with every day – hundreds, and even thousands, of pieces of text data need sorting before they can begin to make sense of all this information. There are more chances of getting lost in a sea of data (which is often unstructured and, therefore, hard to process and sort) than finding what’s relevant.
Sentiment analysis tools allow businesses to make sense of raw text data and learn how customers feel about their brand. This has a wide number of applications across different business areas, from social media monitoring to customer support.
Thanks to sentiment analysis, companies are able to automate some of their processes, like monitoring brand mentions and easily detecting angry customers among hundreds of incoming support tickets. At the same time, they can obtain relevant insights that can be used to improve decision-making. If customers are reporting about bad UX on your platform, for example, you can leverage that information and focus on enhancing your user experience.
These are some of the biggest advantages of sentiment analysis:
- Real-time analysis. You can run sentiment analysis on all the tweets that mention your brand, or on all your incoming customer support tickets, for example, in real-time. By sorting comments and issues into negative and positive, you can easily identify dissatisfied or angry customers and deal with these issues first.
Imagine if there was a sudden surge in negative comments, you’d be able to spot this irregularity straight away with sentiment analysis and take immediate action to deal with the problem.
- Scalability. Going through and sorting every single customer review, survey, social media post, and email can take a lot of time when done manually. It’s also unproductive, especially when faced with large amounts of data. Sentiment analysis, on the other hand, enables you to process large amounts of data in seconds, helping teams save precious time and focus on what they do best.
- Consistent criteria. When humans evaluate the sentiment of a given piece of text, they often disagree, since humans are influenced by their own thoughts, beliefs, and personal experiences. So, while one person might tag a text as negative, another might tag it as neutral, or even positive. Sentiment analysis applies the same criteria to all data, reducing errors, and improving the accuracy of results.
What are Sentiment Analysis Tools?
Sentiment analysis tools help you analyze text conversations and identify the emotions and opinions behind them.
Even though it is possible to create sentiment analysis models from scratch using open source tools, this demands quite a lot of time and resources. Not only do you need to have programming skills, but you also need to understand how machine learning works.
Using a sentiment analysis tool can be a great shortcut if you want to avoid all the hassle and get started right away. SaaS-based sentiment analysis tools provide some of the most interesting solutions.
What are the Benefits of Using a Sentiment Analysis Tool?
As we mentioned before, creating a sentiment analysis model from the ground up can be a very challenging task. If you don’t want to invest time and resources, you can try a sentiment analysis tool. Let’s take a look at some of its advantages:
- Ready to use. These online tools don’t need setup. Unlike open source libraries like Scikit-learn and TensorFlow that require setting up the necessary environment and infrastructure to start working on a model, you can get your sentiment analysis model up and running in just a few minutes with specialized tools.
- Integrations with other tools. Most SaaS-based solutions can be easily integrated with tools like Google Sheets, Zendesk, or Zapier. You can use these tools as a data source and store the results of your sentiment analysis in your cloud.
- No coding skills required. SaaS platforms take care of everything; you just need to use one of the integrations or write a few lines of code (very simple) to connect your data to their API.
- Pre-trained models. Sentiment analysis tools often provide pre-trained models that you can try before choosing the SaaS solution that’s suitable for you.
Best Sentiment Analysis Tools
By now, you are probably eager to try a sentiment analysis tool and explore all the things you can do with it. Here’s a list of the best solutions available online:
- MonkeyLearn: this AI platform focuses on text analysis with machine learning. For a rapid immersion into sentiment analysis, you can try one of the pre-trained models. If you require more accuracy, or you want a model to recognize industry-specific vocabulary or expressions, then you can easily build a custom sentiment analysis model.
- Google Cloud NLP: the Natural Language API is part of the Cloud Machine Learning API family. This Google API provides machine learning pre-trained models and allows you to create customized models to extract sentiment or information about places, events, or people from unstructured text data. The machine learning technology behind this API is the same as Google Search and Assistant.
- IBM Watson: this multi-cloud platform offers a series of APIs for text analysis using Natural Language Processing (NLP). You can run some of them on your data streams, like social media channels or customer support chats. Also, you can directly filter the important messages to a specific representative. The Watson Tone Analyzer, on the other hand, allows you to understand the emotions and communication style behind unstructured text.
- Lexalytics: this modular business intelligence solution provides sentiment analysis to understand how customers feel, but also to get insights on why they feel the way they do. Depending on your needs, you can opt for an on-premise or a SaaS platform. It offers many options for customization, though it’s particularly geared towards data scientists and data analytics companies.
- MeaningCloud: this is a platform for multilingual sentiment analysis and other types of text analysis. Their sentiment analysis API enables users to perform aspect-based sentiment analysis and detect irony or polarity. Also, you can define your own dictionaries to adapt the analysis to the specific vocabulary of your domain.
- Amazon Comprehend: this NLP service for text analysis is part of the Amazon Web Services Infrastructure. It allows you to perform real-time analysis (recommended for small amounts of text) or to create an “analysis job” (for large projects). The sentiment feature classifies a given text as positive, negative, neutral, or mixed, and doesn’t require you to have specific knowledge about machine learning.
- Aylien: the Aylien Text API allows you to run sentiment analysis to understand the polarity of a text and its subjectivity. Another option is to use the Text Analysis platform to build customized sentiment analysis models. Using the API requires coding skills, although its end-to-end platform allows you to start building without machine learning, natural language processing, or coding knowledge.
Now that you’ve got an overview of some of the most popular sentiment analysis tools available on the market, it’s time to figure out which one is the most effective for you. To make this decision, you may want to consider aspects like flexibility, the possibility of building a custom model, personalization options, ease of use, and available integrations.
Out of all the options mentioned above, MonkeyLearn provides the most flexible and user-friendly interface. Also, it boasts many options for customization: you can train your sentiment analysis model using your own training data, decide which algorithm you’d like to use (like support vector machines or naive bayes), and choose different parameters to configure the data training process (you can decide the size of the n-grams or set up the stop-words, for example).
As well being the most user-friendly interface, it’s also optimized for tagging training data in a very fast way, by suggesting tags that allow the model to learn faster (this is called active learning).
Finally, the platform offers different options to debug the model and improve its performance, like model stats (accuracy, F1 score, precision, and recall), information on false positives and false negatives, and a cloud of the most common keywords associated to each of the tags.
Once you’ve built your sentiment analysis model, one of the easiest ways to start analyzing your data is by using some of the available integrations, which allow you to connect easily to your apps. Compared to other sentiment analysis tools, MonkeyLearn provides the largest number of integrations, including Zapier, Google Sheets, Excel, and others.
How to Create a Custom Sentiment Analysis Model?
Building a custom sentiment analysis classifier is the best way to go if you want to improve accuracy, and analyze specific vocabulary or expressions within your field.
With MonkeyLearn, you can create a custom model that fits your own data and criteria. Ready to get started? Just follow the steps in this tutorial.
1. Choose a model type
2. Choose a type of classification
In this case, you’ll choose sentiment analysis:
3. Upload your data
You need to import data to train your sentiment analysis model. You can do this in two ways: by uploading a CSV or Excel file, or by importing data from a third-party source like Zendesk, Twitter, Promoter.io, etc.
4. Train your model
You need to start tagging each opinion as Positive, Negative, or Neutral. This can take a while, as you may need to train your model with several examples before it can start making its own predictions:
5. Test your model
Once you’ve finished tagging your training samples, you can test your sentiment analysis classifier. Click on ‘Run’, paste a text, and see how your model performs:
If you are not satisfied with the results, you can click on the ‘Build’ tab to keep training your model.
6. Put your sentiment analysis classifier to work
Once you are happy with the predictions made by your model, it’s time to let it analyze your data. There are three ways of doing this:
- Process data in a batch: import an Excel or CSV file; your model will run the analysis and give you another file back with the results.
- Connect with the MonkeyLearn API and analyze new data programmatically.
- Use one of the available integrations (Google Sheets, Zapier, Zendesk, Rapidminer) to connect with MonkeyLearn.
Sentiment analysis tools enable organizations to understand the emotions, thoughts, and appraisals behind a text. You can analyze large amounts of text in just seconds, and even perform real-time analysis on tweets, reviews, or any other data source mentioning your brand, allowing you to detect urgent comments instantly.
Even though sentiment analysis models can reach high levels of complexity, getting started is not as complicated as it seems. As we’ve seen during this guide, there are many online tools available that can help you create customized sentiment analysis models in a simple and cost-effective way, saving time and resources, while getting state-of-the-art results. Deciding which one of these tools is right for you will largely depend on the flexibility, ease of use, and customization options that you are looking for.
Ready to take your first steps? Request a demo and our team will help you get started with building your own sentiment analysis model!