Listening to what’s being said about your brand can be invaluable for any business. Humans can identify positive and negative sentiments, identify slang, sarcasm, irony, and more. However, the enormous volumes of chatter on the internet make it difficult to determine the overall public sentiments. No need to get anxious, that is exactly what sentiment analysis tools are for.
Sentiment analysis tools can help you compile and analyze everything that’s being said about your brand. You can determine what makes the customers happy and find the areas that need improvement.
In this blog, we will discuss what sentiment analysis tools are and why every business and brand should use them. Let’s dive in,
Sentiment Analysis Tools: What are They?
Sentiment analysis tools are machine learning and NLP (Natural Language Processing) based software solutions. You can use these tools to automate the analysis of customer feedback and understand what customers think about your brand or its products and services.
These tools analyze emotions, tone, and urgency expressed in customer feedback and categorizes them into positive, negative, or neutral sentiments. It can help you understand what customers like, what they don’t, and which conversations to prioritize.
While there are numerous sentiment analysis tools, not every tool works the same. Some are pretty simple use whereas some require more in-depth knowledge and technical expertise.
Here are a few questions you need to answer for deciding the right tool:
When do you have to get started with sentiment analysis?
Do you want to build your own tool and do you have the time and resources for it?
Or do you want an already developed and tested solution?
If you are still confused, read this article on sentiment analysis to get a better understanding of how it works.
The Best Sentiment Analysis Tools
Before we dive into the paid SaaS sentiment analysis solutions, let’s discuss some popular open-source sentiment analysis options.
Python-based Open-source Sentiment Analysis Tools
Scikit-learn is a popular and powerful open-source toolkit of sentiment analysis. It has text vectorization features for building classifiers such as frequency or tf-idf text vectorizers. Also, Scikit-learn supports numerous machine learning algorithms including vector machines, logistic regression, and naïve Bayes.
NLTK is the most utilized NLP library by Python programmers. It also has a thriving community for support along with a classifier training option.
TensorFlow is a Google platform that offers a combination of basic tools for developing and training neural networks. It also offers text vectorization along with sophisticated cross-word embeddings and basic word frequency.
Keras has built-in support for common types of neural networks, like RNNs and CNNs, and provides stackable neuron layers. Keras is also the base on which Tensorflow or Theano can be built.
PyTorch has attracted wide interest from the academic community because of the projects it has undertaken with several major tech firms and universities, including Uber, Stanford University, the University of Oxford, and Nvidia.
SaaS-based Sentiment Analysis Tools
BytesView is a comprehensive text analysis with a wide range of features including a sentiment analysis tool that is ready to use. Furthermore, it also enables easy integration of data with various in-built integrations such as Zendesk, Zapier, Excel, and Google Sheets. You can also choose from the various custom integration options if needed.
BytesView also enables you to build and train custom sentiment analysis models with data specific to your organization. If you have the resources and time to code, you can use the BytesView API to build your own custom sentiment analysis model. The biggest advantage of building a custom sentiment analysis model is the increased accuracy of the analysis.
MonkeyLearn is another excellent text analysis tool that offers a ready-to-use sentiment analysis solution. It offers plugins for Zendesk, Google Sheets, and more for easy integration of data.
Additionally, if you know how to code, you can use the MonkeyLearn API to build your custom sentiment analysis model. You can use data related to your industry or business organization to train the model and increase accuracy.
Use the MeaningCloud Sentiment Analysis API to perform sentiment analysis. This program does aspect-based sentiment analysis on the user’s input to find out if a specific topic draws positive, negative, or neutral sentiments.
Some of MeaningCloud’s best features include global sentiment detection understanding which elements are opinion and which are facts and determining how the customer felt about every individual sentence in the text.
Lexalytics’s Semantria’s cloud-based API offers sentiment analysis solutions. Lexalytics Salience is perfect for data scientists and architects who need to have full access to the technology or who want to deploy it in an on-premise setting for security reasons.
Regardless of the infrastructure, you select, you’ll have access to the Lexalytics API, a powerful system that can be customized to meet your specific needs, though if you’re not a data scientist, you might find it difficult to understand how the API works.
Know what a new development means for your brand with the Aylien News API. Identify attitudes about topics with various dimensions of sentiment analysis that go beyond just reading stories.
It is possible to set up your own models using the text analysis platform and you won’t have to worry about understanding machine learning or NLP.
The Talkwalker search engine quickly finds information on social media. This tool is designed to automatically discover insights about your brand across social media communications. The results will show what worked, content that resonates, and insight into what audiences like.
Use Quick Search to analyze your social mentions, in 25 languages, for the sentiment! You can stay on top of all the issues as they come up and learn how customers feel about your company or products by utilizing real-time social listening.
You can use the Rosette API to do more detailed sentiment analysis on social media data, as well as find out details about different aspects of how people feel. Like for instance, when customers express positive feelings towards your brand or its products and services.
By lemmatization and morphological analysis, Rosette is able to identify parts of speech. The Rosette sentiment analysis tool can help a company identify 30 different languages in cases where the company operates globally.
Social Searcher tracks keyword usage, hashtags, and personal handles on every social platform. Get an analytics dump, including information about your audience, the most popular hashtags, and influencers who are active on social media.
The free version of the app has a tool that does sentiment analysis on social media data from each platform, to help separate popular posts that are classified as positive and negative.
Clarabridge offers solutions to access the sentiments of your customers, no matter where they leave digital footprints like social media, emails, chats, and surveys. For instance, Clarabridge’s speech analytics service analyses audio for the sentiment. Teams dealing with a lot of calls (e.g., sales and support) can greatly benefit from this.
Awario is a tool that utilizes sentiment analysis to listen to social content (such as tweets, Reddit threads, and Facebook posts) in real-time. You can collect information about your brand’s performance by analyzing the sentiments behind conversations mentioning your brand.
Not only do you have the ability to track specific keyword sentiments, but you can do so even on a long-term basis. It’s important to monitor how keywords in your industry or specific topic and stay updated about the latest developments.
All the above-mentioned sentiment analysis tools are the best that I have come across. However, access your requirements before you choose one to get started. Some tools are ready to use and some require coding. Another thing to note is that not all the tools are customizable. Whereas the tools that are customizable cannot be put to use without a data science background and skills.
BytesView is a combination of both. You can get started with its already developed sentiment analysis model. Or you can use the BytesView API to develop and train custom sentiment analysis models for the organization. Click here for a demo.