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Sentiment Analysis Tool Comparison 2019 : What is the best tool for you?

Updated: May 15, 2020


Sentiment analysis is also known as opinion mining or emotion AI, sentiment analysis is a type of data mining that measures the inclination of people’s opinions through natural language processing (NLP), computational linguistics and text analysis, which are used to extract and analyze subjective information from the Web - mostly social media and similar sources. The analyzed data quantifies the general public's sentiments or reactions toward certain products, people or ideas and reveal the contextual polarity of the information.

Basic Working of sentiment analysis


Companies collect data on sentiment trends and use it to understand how their brand reputation is changing over time. Additionally, sentiment analysis also offers useful insights into customer emotion to make proactive decisions about your company’s future.

Sentiment analysis also helps:

  • Provide insights into your audience: Find out how your customers feel about their brand, products, and services.

  • Measures the outcomes of marketing campaigns: Review the success of marketing programs based on changes in customer sentiment.

  • Supports customer service: Listening for changes in customer reactions may help brands to offer quicker resolutions to problems over social media.

  • Supplements positive PR practices: Tracking sentiment helps brands to keep track of any negative mentions or comments they need to address quickly. The faster you know about a problem, the quicker you can stop the issue from spreading.


Sentiment is an emotion, feeling or opinion. On social media, a person’s sentiment is evident in their tone or the way they talk about a brand. Sentiment analysis is useful in social media monitoring, as it offers an overview of public opinion around your brand.

With a tool for sentiment analysis, companies extract helpful insights from social data. Many social listening tools allow businesses to filter mentions by sentiment to focus on the issues that need to be dealt with as quickly as possible. For instance, sentiment analysis is essential when businesses need to:

  • Watch for PR and social crises

  • Find and assist unhappy customers

  • Respond to negative feedback



This is where things get tricky, the best tool depends on your requirement and how you plan to use it. Seeking professional help from companies like web-stepup is recommended unless you really know what you are doing.

Here are some tools that help with Sentiment Analysis.

SAS Sentiment Analysis automatically rates and classifies opinions expressed in electronic text. It collects text inputs from websites, social media outlets and internal file systems, and then puts them in a unified format to assess relevance to predefined topics.

Reports identify trends or emotional changes, and an interactive workbench allows subject-matter experts to refine sentiment models. The solution automatically scores input documents as they’re received, providing real-time updates about sentiment changes.

SAS Sentiment Analysis is designed for marketing, public relations, service and development departments charged with enhancing the customer experience, evaluating new products or managing logistical operations for improvement.

SAS Sentiment Analysis Tool Workbench

List of Pros And Cons to Guide you better.


  • Evaluate sentiment and monitor changes over time- The software automatically extracts sentiments in real time or over a period of time with a unique combination of statistical modelling and rule-based natural language processing techniques

  • Identify feedback sources to define new targets - By actively monitoring internal collections and combining that with information from social networking sites, you can see where you're being discussed and what's being said.

  • Promote discovery with a closed-loop, integrated analysis environment -With ongoing evaluations, you can refine models and adjust classifications to reflect emerging topics and new terms relevant to your customers, organization or industry.

  • Continuously improve customer experience and competitive position The software searches for and evaluates internal and external content about your organization and competitors, identifying positive, negative, neutral and "no sentiment" texts – quantifying perceptions in the market.


  • Is an expensive tool

  • SAS is not open source

  • Improper Graphic representation

  • Text Mining is a little troublesome

Comparison Graph

For many years, “data” effectively meant numbers and figures. Today, many organizations have discovered great insights through text mining, extracting information from the qualitative, textual content. Online reviews, social media chatter, call centre transcriptions, claim forms, research journals, patent filings, and many other sources, all become rich resources that can be tapped through data science to advance your business and organizational mission. Use these insights to improve marketing, product development, risk management and more.

Rapidminer Features

List of Pros And Cons to Guide you better.


  • No coding required

  • The RapidMiner provides a rich set of Machine Learning algorithms for Data Mining tasks, along with a comprehensive set of operators (functions) for data pre-processing. RapidMiner has a repository containing hundreds of machine learning algorithms and functions.

  • Work-flow visualization - the interface allows you to clearly see what the steps are and where any failures occur.

  • Training is easy, the tool is intuitive and there's a lot of videos on the internet. The community is very active.


  • No coding required-Challenging to use for coders. Although it does contain Java/Python modules you must use flow programming interface.

  • Sharing RapidMiner Studio analysis is not easy.

  • With large-sized data sets, there are processing speed issues with a few of the operators.

  • Certain terminology used by the tool can confuse a new user.

Sentiment Analysis is MeaningCloud's solution for performing detailed multilingual sentiment analysis of texts from different sources.

It identifies the positive, negative, neutral polarity in any text, including comments in surveys and social media. It also extracts sentiment at the document or aspect-based level. In order to do this, the local polarity of the different sentences in the text is identified and the relationship between them evaluated, resulting in a global polarity value for the whole text.


  • It extracts aspect-based sentiment.

  • It distinguishes facts and opinions.

  • It detects irony and polarity disagreement.

  • Users can define their own dictionaries and detect the sentiment of the elements included.

  • Users can define their own sentiment model to adapt the analysis to their sub domain.

MeaningCloud Features

List of Pros And Cons to Guide you better.


  • Detection of irony - identifies comments in which what is expressed is the opposite of what is said.

  • Graduated polarity - distinguishes very positive and very negative opinions, as well as the absence of sentiment.

  • Identification of opinions and facts- distinguishes between the expression of an objective factor subjective opinion.


  • No coding required-Challenging to use for coders. Although it does contain Java/Python modules you must use flow programming interface.

  • Sharing RapidMiner Studio analysis is not easy.

  • With large-sized data sets, there are processing speed issues with a few of the operators

  • Certain terminology used by the tool can confuse a new user

Social Mention Fetures

List of Pros And Cons to Guide you better.


  • Reduce social mistakes: it helps to find and respond to inquiries/comments about your brand, which is quite difficult when there are multiple mentions.

  • Score report: Helps to keep score of the online passion for your brand.

  • Social influence: reaches all mentions on your brand throughout the internet.


  • The tool has problems with sub-filtering keywords by sentiment or by source.

  • It doesn’t offer the power for filtering content sources and outputting the results through API.

  • It has no advanced features in the API addressing the problems with sub filtering keywords by individual source.

Discover what people say about your brand & take action in real time. Engage discussions relevant to your business with one click of the mouse.

Brand24 features

List of Pros And Cons to Guide you better.


  • The reporting is clean and client facing friendly! The reporting is clean and client facing friendly

  • Easy-to-use interface with comprehensive features for tracking and monitoring important keywords and brands Slack integrations

  • It's super easy to use.


  • The mobile app isn't perfect

  • real-time update plan is quite pricey

  • Phrases they monitor by default could be shared for customers.

Clarabridge is the most complete and reliable customer intelligence app that understands clients’ requirements, demands and feelings and allows client experience management with proper insights and tips from the industry leaders. It collects data from different sources like multiple survey types, social media, emails, voice, chats, contact center agent notes and warranty notes and then deciphers and derives the meaning of those data using its Natural Language Processing (NLP) solution which incorporates text analytic, linguistic categorization, context-sensitive sentiment scrutiny and emotion recognition.


List of Pros And Cons to Guide you better.


  • It assimilates with different social media sites like Facebook, Trip Advisor, Twitter.

  • NPL – This technology used by Clarabridge text analytics guarantees about 90% accuracy.

  • Clarabridge reports, consoles and alerts are supported by tablets, mobile phone and desktop computers.

  • With Clarabridge, Clients can optimize their marketing campaigns, call centre performance, product enhancements, worker drill, tactical preparation and much more.


  • Customer support is not helpful/cooperative at all

  • The interface is needlessly complicated

  • Steep pricing makes it not a good fit for smaller companies

  • Doesn’t keep up with real-time events and leads to operational issues

MonkeyLearn is an AI-based software that is used to analyze text with Machine Learning to automate commercial workflows. Analyze Text Data and response by aspects of sentiment, emotion, opinion units.

Clients use MonkeyLearn to classify and extract actionable data from raw texts like emails, chats, web pages, documents, tweets and more. The software integrates with Google Sheets, Zapier, Zendesk, Rapidminer, and more.

List of Pros And Cons to Guide you better.


  • No Coding required.

  • Has tutorials to guide new user.

  • It's super easy to use.


  • It is costly.

  • Lacks combustibility.

  • Limitation on number of queries as per plan.


Adjusting the functionality of the APIs/tool that you have chosen to guide you better in your business situations will help in achieving maximum accuracy in the analysis.

A lot of the tools feature a set of customization allowing users to alter the functionality of the APIs/Tools to their requirement in an easy way and in some cases without programming. They allow the user to create domain-specific dictionaries and models to provide optimal results.

The output of your text analysis system depends on two things technology and algorithms employed, for if a certain topic is not included in the resources used for extraction, it won’t be detected. It is impossible for a standard product to include all topics, themes, etc. of any possible application. Including the required resources for each case allows the tool to achieve an optimum quality score in the analysis.

In a lot of these tools, there is an option to automate some of the processes so using the automated process should be preferred over the manual effort so as to minimalism the human error and to increase the efficiency of the tool

There is also a learning curve involved for both the tool and the person using the tool.Automation slowly understands how you use the tool and tries to adjust itself to your use. Just like a person slowly adjusts himself to work the tool better.


The age of getting expressive insights from social media data is here with the advancements in technology. It’s time for your company to move beyond overall sentiment and count based metrics. In this day and age you can use insights from AI based computer programs and help your business reach new levels.

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