In today’s data-rich landscape, organizations must leverage the power of artificial intelligence (AI) sentiment analysis to make informed decisions based on public opinion, customer feedback, and market trends. With so many AI-powered sentiment analysis tools available, the question is, which ones are best suited for enabling data-driven decision-making?
In this comprehensive guide, we will look at the Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making based on their accuracy, customization, language support, and scalability. Whether you’re tracking brand reputation, analyzing customer sentiment, or gauging market trends, these tools provide powerful capabilities for extracting actionable insights from text data, allowing businesses to stay ahead in an increasingly competitive and opinionated landscape. Read more on Futureaitoolbox.com
In the digital age, where information flows quickly and abundantly, understanding public sentiment has become critical for businesses, governments, and organizations. Sentiment analysis using artificial intelligence (AI) has emerged as a game changer in this field. AI-driven sentiment analysis provides valuable insights into public opinion by analyzing text data from social media, reviews, news articles, and other sources. This allows for informed decision-making and proactive response strategies.
AI-driven sentiment analysis is an effective tool for analyzing and interpreting human sentiments expressed in written or spoken language. This technology can identify the sentiment of text data as positive, negative, or neutral, providing valuable insights into customers’ feelings and attitudes. It can quickly process large amounts of text, making it a valuable resource for businesses looking to better understand customer behavior and identify emerging threats.
AI sentiment analysis has a significant impact, with studies showing that 95% of consumers read online reviews before making a purchase decision, and 58% are willing to spend more money on products recommended by positively reviewed brands1. Furthermore, 86% of consumers are willing to pay more for a positive customer experience, emphasizing the importance of understanding and addressing customer feedback.
According to a report by Grand View Research, the global sentiment analysis market size was valued at USD 3.23 billion in 2020 and is projected to reach USD 11.73 billion by 2027, with a CAGR of 19.6% during the forecast period.
A study by IBM found that 70% of customer service executives believe sentiment analysis and text analytics are essential for understanding and improving customer experience.
Research by Gartner suggests that by 2025, 65% of the world’s population will have their personal data covered under modern privacy regulations, increasing the need for AI-driven sentiment analysis to navigate data privacy concerns effectively.
Case Studies and Examples of AI-Driven Sentiment Analysis
Brand Monitoring and Reputation Management:
Example: Coca-Cola uses sentiment analysis to monitor brand mentions on social media platforms. By analyzing sentiment trends, they can assess public perception, identify potential PR crises, and tailor their marketing strategies accordingly. For example, if negative sentiment arises as a result of a product issue, they can quickly address it before it worsens.
Financial Market Analysis:
Example: Hedge funds and investment firms use sentiment analysis algorithms to identify market sentiment indicators in news articles, social media posts, and financial reports. Understanding investor sentiment allows them to make data-driven decisions about purchasing, selling, or holding stocks and commodities. For example, if sentiment analysis predicts a negative outlook for a specific stock, investors may decide to sell or hedge their positions.
Customer Feedback and Product Development:
Example: Airbnb uses sentiment analysis to analyze guest reviews and feedback. By identifying common themes and sentiment patterns, they gain valuable information about guest preferences, pain points, and areas for improvement. This enables Airbnb to improve the user experience, refine their offerings, and remain competitive in the hospitality industry.
Political Campaigns and Public Opinion:
Example: During election campaigns, political candidates use sentiment analysis to gauge public support for their policies, speeches, and campaign messages. By monitoring social media conversations and news coverage, candidates can assess voter sentiment in real time, identify key issues, and adjust their campaign strategies to better resonate with voters.
Healthcare and Patient Sentiment Analysis:
Example: Healthcare organizations use sentiment analysis to analyze patient feedback, reviews, and social media discussions about their services. Understanding patient sentiment enables healthcare providers to identify areas for improvement, increase patient satisfaction, and ultimately improve the quality of care they provide.
These examples demonstrate the broad range of applications for AI-driven sentiment analysis across industries, as well as its effectiveness in understanding and responding to public sentiment in real time. As technology advances, sentiment analysis will undoubtedly remain an important tool for businesses and organizations looking to stay competitive in an increasingly interconnected and opinion-driven world.
*Source: Grand View Research report titled “Sentiment Analysis Market Size, Share & Trends Analysis Report By Deployment (On-premise, Cloud), By Component (Solution, Services), By Application, By Region, And Segment Forecasts, 2020 – 2027” and Link: Grand View Research Report
IBM’s Institute for Business Value report titled “Analytics: The real-world use of big data in customer service”
Source: Gartner’s research and projections on data privacy regulations
Understanding Sentiment Analysis Tools and Score Measurement
A sentiment analysis tool is a software application or algorithm that analyzes text data using natural language processing (NLP) and machine learning techniques to determine the sentiment expressed therein. The sentiment is typically classified as positive, negative, or neutral. Sentiment analysis tools are used in a variety of industries for purposes such as brand monitoring, customer feedback analysis, market research, and social media monitoring.
Here’s how sentiment analysis scores are usually measured:
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Pre-processing: Tokenization (breaking text into individual words or tokens), punctuation removal, text conversion to lowercase, and removal of stop words (commonly used words like “and”, “the”, “is”, etc., which have no significant meaning for sentiment analysis).
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Sentiment Lexicon: Many sentiment analysis tools use sentiment lexicons or dictionaries. These lexicons include words or phrases labeled with a sentiment (positive, negative, or neutral). Some lexicons also assign scores to words indicating the intensity of sentiment.
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Rule-Based Approaches: Rule-based sentiment analysis uses predefined rules or patterns to determine sentiment. For example, if a sentence contains words like “happy,” “joyful,” or “excited,” it is most likely classified as positive sentiment.
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Machine Learning: Machine learning methods train models on labeled data (text with sentiment labels) to discover patterns and relationships between words and sentiments. Naive Bayes, Support Vector Machines (SVM), and Recurrent Neural Networks (RNNs) are examples of machine learning algorithms commonly used for sentiment analysis.
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Sentiment Score Calculation: After analyzing the text data, the sentiment analysis tool assigns a sentiment score to each piece of text. This score represents the level of positivity, negativity, or neutrality expressed in the text. The sentiment score can be either a numerical value (ranging from -1 to 1) or a categorical label (positive, negative, or neutral).
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Aggregation: When sentiment analysis is applied to longer texts or multiple documents (e.g., customer reviews, social media posts), sentiment scores can be combined to produce an overall sentiment score for the entire text or collection of texts. Aggregation methods include simple averaging, weighted averaging based on document length, and more sophisticated techniques.
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Evaluation: Finally, the performance of the sentiment analysis tool is assessed using metrics such as accuracy, precision, recall, and F1-score. These metrics compare the tool’s sentiment prediction performance to human-labeled data.
The sentiment analysis score can be calculated using any of the following methods:
Method 1: The percentage of positive mentions among total mentions Sentiment Analysis Score = (Number of Positive Mentions / Total Mentions) x 100 For example, if a brand received 100 mentions, 60 of which were positive, its sentiment analysis score would be 60%.
Method 2: The percentage of positive mentions among mentions that include sentiment, excluding neutral mentions. Sentiment Analysis Score = (Number of positive mentions / total number of positive and negative mentions) x 100 For example, if a brand received 100 mentions, with 60 positive and 20 negative, the sentiment analysis score would be 75%.
These methods can be used to calculate sentiment analysis scores for specific time periods, such as a day, week, or month, as well as to track sentiment changes over time.
Overall, sentiment analysis tools provide useful information about public opinion, customer feedback, and market trends, allowing businesses to make data-driven decisions and tailor their strategies accordingly.
Example 1: Movie Review Analysis
Text: “The movie was fantastic! The acting was superb, and the plot kept me engaged throughout.”
Steps:
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Pre-processing: Tokenization, removing punctuation, converting to lowercase.
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Sentiment Lexicon: Words like “fantastic”, “superb”, and “engaged” are typically associated with positive sentiment.
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Machine Learning: If using a machine learning approach, the model would learn from labeled movie reviews where similar phrases were labeled as positive.
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Sentiment Score Calculation: The sentiment analysis tool assigns a high positive sentiment score to this review.
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Example 2: Product Review Analysis
Text: “The product arrived damaged, and the customer service was non-responsive. Very disappointing experience.”
Steps:
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Pre-processing: Tokenization, removing punctuation, converting to lowercase.
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Sentiment Lexicon: Words like “damaged”, “unresponsive”, and “disappointing” are typically associated with negative sentiment.
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Machine Learning: The model learns from labeled product reviews where similar complaints were labeled as negative.
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Sentiment Score Calculation: The sentiment analysis tool assigns a low negative sentiment score to this review.
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Example 3: Social Media Post Analysis
Text: “Just booked my dream vacation! So excited to explore new places and cultures.”
Steps:
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Pre-processing: Tokenization, removing punctuation, converting to lowercase.
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Sentiment Lexicon: Words like “excited” and “dream vacation” are typically associated with positive sentiment.
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Machine Learning: The model learns from labeled social media posts where similar expressions were labeled as positive.
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Sentiment Score Calculation: The sentiment analysis tool assigns a high positive sentiment score to this post.
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In each example, the sentiment analysis tool examines the text data, identifies relevant words and phrases, and calculates a sentiment score based on their presence and context. The tool determines the sentiment expressed in the text, whether using sentiment lexicons, rule-based approaches, or machine learning, allowing organizations to better understand public opinion, customer feedback, and user sentiment on social media.
Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making
| Tool | Description |
|---|---|
| Awario | A social listening tool that offers sentiment analysis for brand mentions on social media, forums, news sites, and more. It generates graphs and word clouds to give a visual representation of brand sentiment. |
| Aylien | A news API that offers sentiment analysis for real-time news events and stories, detecting the severity and impact of news on a brand’s reputation. |
| Brand24 | A social media monitoring tool that offers sentiment analysis for brand mentions on social media, filtering positive and negative mentions, and tracking influencer scores. |
| Brandwatch | A social listening tool that offers sentiment analysis for brand mentions on social media, filtering positive and negative mentions, and tracking sentiment trends over time. It supports multilingual sentiment analysis. |
| Clarabridge | A customer experience platform that uses AI to analyze text from multiple sources, including social media, surveys, reviews, SMS, emails, voice conversations, and more. It offers sentiment analysis for text, speech, and video. |
| Critical Mention | A media monitoring tool that offers sentiment analysis for brand mentions on TV, radio, online news, and social media. It supports multilingual sentiment analysis. |
| Hootsuite Insights | A social media management tool that offers sentiment analysis for brand mentions on social media, filtering positive and negative mentions, and tracking sentiment trends over time. It supports multilingual sentiment analysis. |
| Hubspot’s Service Hub | A customer service platform that offers sentiment analysis for customer support interactions, assigning sentiment scores to quantify emotions and analyze text in multiple languages. |
| Lexalytics | A text analytics platform that offers sentiment analysis for brand mentions on social media, filtering positive and negative mentions, and tracking sentiment trends over time. It supports multilingual sentiment analysis. |
| MeaningCloud | A text analytics platform that offers aspect-based sentiment analysis for brand mentions on social media, filtering positive and negative mentions, and tracking sentiment trends over time. It supports multilingual sentiment analysis. |
| MonkeyLearn | A text analytics platform that offers sentiment analysis for brand mentions on social media, filtering positive and negative mentions, and tracking sentiment trends over time. It supports multilingual sentiment analysis. |
| ParallelDots | A sentiment analysis tool that uses LSTM algorithms to classify a text blob’s sentiment into positive or negative. It supports 14 different languages and offers a free account with a free forever plan. |
| Rapidminer | A data science platform that offers text mining in unstructured data to figure out sentiment. It supports multilingual sentiment analysis. |
| Repustate | A sentiment analysis tool that offers sentiment analysis for brand mentions on social media, filtering positive and negative mentions, and tracking sentiment trends over time. It supports multilingual sentiment analysis and offers a free trial. |
| Rosette | A sentiment analysis tool that offers sentiment analysis for brand mentions on social media, filtering positive and negative mentions, and tracking sentiment trends over time. It supports multilingual sentiment analysis and offers a free trial. |
| Talkwalker’s Quick Search | A social media search engine that offers sentiment analysis for brand mentions on social media, filtering positive and negative mentions, and tracking sentiment trends over time. It supports multilingual sentiment analysis. |
Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making
As businesses strive to provide customer-centric experiences and improve their brand marketing strategy, monitoring customer sentiment on social media has become a top priority. As a result, an increasing number of businesses are adopting user-friendly and powerful sentiment analysis tools to achieve this goal. So lets dive into the Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making:
1. Awario
Awario is a powerful sentiment analysis tool that uses social media to detect user sentiment and mentions in real time. It is best suited for businesses or individuals who are comfortable working with figures and numbers.
The tool has an interactive user interface that categorizes sentiments according to brand, topic, and keyword. Furthermore, the dashboard displays negative feedback about your rivals or competitors. Awario covers most online sources, such as websites, blogs, forums, and social media platforms.
Key Features:
Monitors social media for mentions and sentiments in real-time
Provides an interactive user interface that categorizes sentiments based on brand, topic, and keywords
Displays the negative feedback for your rivals or competitors
Encompasses a wide range of online sources, including diverse websites, blogs, forums, and social media platforms
Pricing Model & Free Trial Period:
Awario’s pricing starts at $29 per month for the Starter plan, $89 per month for the Pro plan, and $299 for the Enterprise plan. Prices for yearly plans begin at $290. Awario provides a 14-day free trial.
Best For: Awario is ideal for companies or individuals who are comfortable with numbers and want to track social media mentions and sentiment in real-time.
User Reviews: User feedback on Awario’s sentiment analysis tool is positive, with praise for its accuracy, ease of use, and real-time monitoring capabilities. However, some users have reported that the tool struggles to handle sarcasm, irony, slang, and context.
Despite these limitations, Awario’s sentiment analysis tool remains a popular choice for businesses and individuals seeking to understand customer sentiment and make informed decisions. [Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making
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2. Aylien
Aylien is a sentiment analysis tool that focuses on tracking and analyzing sentiment in news content. It uses artificial intelligence to monitor, organize, and analyze sentiment in news content, making it an invaluable tool for public relations and communications teams looking to stay on top of trends and gauge public opinion and perception of brands and topics. Aylien’s sentiment analysis feature is a key component of the tool, as it determines the sentiment behind each individual entity discussed in a single news piece.
Key Features:
Monitors and analyzes sentiment in news content
Uses AI to monitor, organize, and analyze sentiment
Determines the sentiment behind each individual entity discussed in a single news piece
Pricing Model & Free Trial Period:
Aylien’s pricing model includes a free tier with 1,000 requests per day. For higher volume use, Aylien’s Text Analysis API has three paid tiers: $199 for 6,000 daily requests, $649 for 80,000 daily requests, and $1,399 for 180,000 daily requests.
Best For:
Aylien is ideal for real-time monitoring of social media mentions and sentiments, especially for those in public relations and communications.
User Reviews and Ratings:
Aylien has received mostly positive feedback from users, who praise the tool’s accuracy, ease of use, and real-time monitoring capabilities. However, some users have reported that the tool struggles to handle sarcasm, irony, slang, and context.
Despite these limitations, Aylien’s sentiment analysis tool remains a popular choice for businesses and individuals seeking to understand customer sentiment and make informed decisions.
3. Brand24
Brand24 is an AI-powered media monitoring and social listening tool that collects and analyzes online conversations about brands and competitors. It provides sentiment analysis to assess brand reputation and perception among consumers as expressed on various online sources such as social media, product reviews, blogs, and news sites.
Brand24’s sentiment analysis tool automatically analyzes text, identifying negative and positive words, and categorizing them accordingly. It enables users to filter mentions based on sentiment, such as viewing only positive comments, and provides information about sentiment trends over time.
Key Features:
Monitors and analyzes online conversations about brands and competitors
Provides sentiment analysis to gauge brand reputation and perception
Automatically tags mentions with negative and positive sentiment
Allows filtering of mentions based on sentiment
Offers insights into sentiment trends over time
Pricing Model & Free Trial Period:
Brand24 provides a free two-week trial to explore its features, which include sentiment analysis. The pricing model is divided into tiers based on the number of requests per day, beginning at $199 for 6,000 requests per day.
Best For: Brand24 is ideal for businesses and individuals seeking to monitor and analyze online conversations about their brand, competitors, and industry to understand customer sentiment and perception.
User Reviews: User feedback and ratings for Brand24 are generally positive, with users praising its affordability, ease of use, and real-time monitoring capabilities. Some users have noted the tool’s effectiveness in brand monitoring and sentiment analysis, but there may be occasional challenges in dealing with sarcasm and irony in sentiment detection. [Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making]
4. Brandwatch
Brandwatch is a social media listening and analytics tool that includes sentiment analysis as a core feature. Brandwatch allows users to monitor and analyze mentions of their brand, product, or service, as well as mentions of competitors, in order to understand customer sentiment and perception.
The tool uses advanced machine learning algorithms and natural language processing techniques to analyze text in real time and assign sentiment, allowing users to quickly identify positive, negative, and neutral mentions.
Key Features:
Real-time monitoring
Robust analytics
Ability to discover real-time discussions about a brand
Alert system that notifies users of significant shifts in discussion volume or brand-specific mentions
Pricing Model & Free Trial Period:
Brandwatch provides a 14-day free trial, and monthly pricing begins at $99 for an individual plan, $179 for a team plan, $249 for a pro plan, and $499 for an enterprise plan.
Best For: Brandwatch is ideal for businesses and individuals seeking to monitor and analyze online conversations about their brand, competitors, and industry to understand customer sentiment and perception.
User Reviews: Brandwatch has received mostly positive feedback, with users praising its affordability, ease of use, and real-time monitoring capabilities. However, some users have reported difficulty handling sarcasm and irony in sentiment detection. Despite these limitations, Brandwatch remains a popular choice for businesses and individuals seeking to understand customer sentiment and make informed decisions.
5. Clarabridge
Clarabridge offers tools that provide metrics about your customers’ feelings in various types of data, such as social media, emails, chats, and surveys. The tool uses an 11-point scale to index the sentiment of collected content, accounting for grammar, context, industry, and source when scoring a piece of text.
Key Features:
Sentiment analysis tool for social media and other customer interaction data
Utilizes an 11-point scale for sentiment indexing
Takes into account grammar, context, industry, and source while scoring a piece of text
Integrates with Lithium, NM Incite Buzzmetrics, and Radian6 for direct access to social media data
Provides real-time APIs for multi-verbatim access
Offers French and Portuguese NLP, including advanced sentiment analysis
Provides word clouds, enhanced charting, and dashboards for data analysis
New APIs for real-time multi-verbatim processing and project creation
Offers a self-service SaaS basis with a limited 2,000 free verbatim trial
Pricing Model & Free Trial Period: Clarabridge’s users can take advantage of a limited 2,000 free verbatim trial offer for Clarabridge Pro.
Best For: Clarabridge is ideal for businesses looking to gather customer feedback and identify positive, negative, or neutral opinions in their customer interaction data. [Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making]
6. Critical Mention
Critical Mention is a platform for real-time media monitoring, analyzing online discussions across multiple platforms. Its primary function is to assist businesses in staying up to date on what is being said about their brand, industry, or competitors. Critical Mention provides social listening tools that help businesses gain insights into customer sentiment and trends, allowing them to respond quickly and appropriately. It also helps to save time on manual media monitoring by providing automatic alerts and detailed reporting features.
Critical Mention provides sentiment analysis, allowing users to quickly determine whether mentions are positive, negative, or neutral. The tool’s robust analytics reveal how frequently people discuss a brand and its competitors, as well as social media reach and top influencers.
Key Features:
Real-time media monitoring platform
Tools for social listening
Automated alerts and comprehensive reporting functionalities
Sentiment analysis for discerning positive, negative, or neutral mentions
Comprehensive data coverage and accurate sentiment interpretation
Pricing Model & Free Trial Period:
Critical Mention costs about $4,000 per year. However, for businesses looking for low-cost media monitoring solutions, Brand24 provides competitive pricing and a robust set of features that provide exceptional value. Brand24 also provides a free trial for users to test out its features, which include sentiment analysis.
Best For: Critical Mention is ideal for businesses looking for real-time media monitoring and analysis of online conversations related to their brand, industry, or competitors. It is ideal for businesses that want to stay informed about customer sentiment and trends so that they can respond effectively.
7. Hootsuite Insights
Hootsuite Insights is a powerful social listening tool that uses sentiment analysis to help businesses monitor and analyze online conversations about their brand, industry, and competitors. It provides customizable dashboards and reports, making it easier to monitor sentiment over time and share findings with key stakeholders.
Insights enables users to track sentiment across multiple social media platforms, news articles, support tickets, and emails, providing a complete picture of customer sentiment.
Key Features:
Real-time social media monitoring and sentiment analysis
Customizable dashboards and reports for tracking sentiment trends
Integration with social media platforms, news articles, support tickets, and emails
AI-powered sentiment classification for accurate analysis
Alerts for significant changes in sentiment
Access to a vast network of 150 million social data sources
Hashtag tracking and support for multiple languages
Advanced analytics dashboard for in-depth reporting
Pricing Model & Free Trial Period:
Hootsuite Insights is available to enterprise plan customers and higher. The pricing model for Insights is not explicitly stated, but users can take advantage of a limited 2,000 free verbatim trial offer for Clarabridge Professional.
Best For: Hootsuite Insights is ideal for businesses seeking to track and analyze sentiment across social media, news, and other online sources. It is ideal for organizations that see social sentiment analysis as a critical component of their strategy.
8. Hubspot's Service Hub
Hubspot’s Service Hub is a comprehensive customer service and support solution that helps businesses provide exceptional customer experiences. It includes ticketing systems, live chat, conversational bots, email sequences, and advanced reporting and analytics tools. The Service Hub is available in a variety of tiers, including a free tier for businesses starting out with customer service and higher tiers for more advanced requirements.
Key Features:
Ticketing systems
Live chat
Conversational bots
Email sequences
Advanced reporting and analytics
Sentiment analysis using NLP
Visualization of sentiment data on dashboards
Automation based on sentiment analysis results
Pricing Model & Free Trial Period:
Hubspot’s Service Hub has several tiers, including a free option for basic customer service needs. Pricing for higher tiers is determined by the business’s specific features and requirements. Businesses can explore the platform’s capabilities through free trials.
Best For: Hubspot’s Service Hub is ideal for businesses of any size looking to improve customer service and support. It is appropriate for organizations that want to improve customer experiences, streamline service processes, and use sentiment analysis to gain better customer insights. [Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making]
9. Lexalytics
Lexalytics is an intelligent platform that uses machine learning and natural language processing (NLP) to conduct advanced text analysis. It provides a sentiment analysis app called Semantria API, which enables users to conduct sentiment analysis in the cloud. The platform is designed to handle a wide range of technical issues and requirements, making it an adaptable solution for businesses seeking to measure customer sentiment.
Key Features:
Social media monitoring and sentiment analysis in the cloud
Boolean queries, sentiment phrases, and custom entities
Text analysis and NLP software libraries integration
Part of Speech tagging and text summarization
Pricing Model & Free Trial Period:
Lexalytics’ pricing model is subscription-based, with three tiers: starter, growth, and advanced. The Starter tier costs $1500 per month, the Growth tier costs $2500, and the Advanced tier costs $3500 per month.
Pricing is determined by the features and usage requirements of the business. Lexalytics offers a free trial of its Semantria API, allowing users to process up to 20,000 documents, tweets, or survey responses for free.
The free trial period is not explicitly stated, but it is available for users to test the platform’s capabilities.
Best For Lexalytics is ideal for businesses that want to measure customer sentiments and conduct in-depth text and sentiment analysis. Its platform has advanced NLP capabilities, making it ideal for businesses that need sophisticated text analysis.
User Reviews and Ratings Lexalytics has a G2 rating of 4.3 out of 5 stars, indicating positive user feedback and ratings1. Users have praised the platform for its highly customizable text analytics, scalability, and ability to extract useful insights from text data. Users have also praised the platform’s sentiment analysis capabilities for their accuracy and ability to identify customer intentions in text.
10. MeaningCloud
MeaningCloud is a text analytics platform that provides advanced sentiment analysis capabilities to help businesses understand and interpret customer opinions, emotions, and attitudes based on written or spoken feedback. The platform converts subjective customer sentiment into actionable insights, allowing businesses to better respond to customer perceptions.
It offers a variety of text analytics solutions that use machine learning, statistics, computational linguistics, data mining, and information retrieval techniques to extract valuable information from text, discover patterns, and interpret results.
Key Features:
Aspect-based sentiment analysis
Classification of feedback into positive, negative, or neutral categories
Identification of emotions, urgency, and intentions from text data
Integration of linguistic resources for accurate analysis
Techniques encompassing machine learning, statistics, and computational linguistics
Pricing Model & Free Trial Period:
For on-premises text analytics deployments, MeaningCloud provides a customized pricing model based on perpetual licenses. The total cost depends on the software modules (APIs) that will be used. It offers a free trial of its public APIs with 40,000 requests per month, after which it charges $0.003 for each additional request.
The platform provides four different payment plans to cover scenarios with more stringent requirements, with prices beginning at $25 for 1,000 requests per day, $50 for 10,000 requests per day, and $100 for unlimited requests. The platform also provides a free plan for text summarization with 100 requests per day.
Best For: MeaningCloud is ideal for businesses of all sizes that want to understand and interpret customer sentiments, emotions, and attitudes through text data. It is appropriate for organizations that want to improve customer satisfaction, enhance customer experiences, and make data-driven decisions based on customer feedback. [Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making]
11. MonkeyLearn
MonkeyLearn is an artificial intelligence platform that allows users to analyze text using machine learning, automating business workflows and saving hours of manual data processing.
The platform serves small and medium businesses in a variety of industries, including marketing, sales, customer service, data analysis, and development. Customers including Clearbit, Segment, and Drift use MonkeyLearn to efficiently extract actionable insights from text data.
MonkeyLearn provides a user-friendly graphical interface through which users can perform custom text classification and extraction analyses using machine learning models such as topic detection, sentiment analysis, and keyword extraction. The platform can be easily integrated with hundreds of applications without requiring coding, making it accessible to users with limited technical knowledge.
Key Features:
AI platform for text analysis with machine learning
Sentiment analysis for classifying text as positive, negative, or neutral
Customizable models for specific industry needs
Data visualization tools for analyzing results
User-friendly interface requiring no coding skills
Pricing Model & Free Trial Period:
MonkeyLearn’s pricing model is subscription-based, starting at $299.00 per month. The platform provides a single plan as well as custom plans based on customer requests. MonkeyLearn does not have a free plan, but it does provide a free trial for users to test out its platform and capabilities.
Best For: MonkeyLearn is ideal for small and medium businesses seeking to automate text analysis and gain valuable insights from text data. It is appropriate for users from a variety of departments, including marketers, salespeople, customer service representatives, data analysts, and developers.
12. ParallelDots
ParallelDots is an applied AI research group that provides a sentiment analysis tool as part of its services. The sentiment analysis API uses Long Short Term Memory (LSTM) algorithms to determine whether a text blob is positive or negative.
The tool is trained on social media and news data, so it can handle both casual and formal language. It supports 14 languages and provides a free account with a free lifetime plan for testing APIs, creating MVPs, and completing personal projects.
Key Features:
Utilizes Long Short-Term Memory (LSTM) algorithms to categorize the sentiment of text blobs into positive or negative
Trained on both social media and news data to accommodate variations in casual and formal language
Supports 14 different languages
Offers a free account with a free forever plan for testing APIs, building MVPs, or finishing personal projects
Pricing Model & Free Trial Period:
ParallelDots provides a free account with a free lifetime plan for testing APIs, developing MVPs, and completing personal projects.
For more advanced features, they provide five different plans ranging from $79 to $499 per month, as well as a custom plan based on the customer’s specifications.
Best For: ParallelDots’ sentiment analysis tool is ideal for businesses that need to analyze text data in multiple languages with high accuracy in sentiment classification. It is also appropriate for businesses seeking a scalable solution for analyzing large amounts of text data.
User Reviews: ParallelDots has received positive feedback from users for its user-friendly interface, accurate sentiment classification, and multilingual support. However, some users have complained that the tool is slow at times and that the documentation could be improved. Overall, ParallelDots’ sentiment analysis tool is a top-rated option for businesses that need to analyze text data in multiple languages. [Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making]
13. Rapidminer
RapidMiner is a data science and data mining platform that lets you extract, transform, and load data to gain insights. It is a powerful predictive analytics tool with a GUI editor for creating custom processes and models. The software can import data from local files and databases and allows users to perform basic data visualization and statistical analysis.
RapidMiner can be used for sentiment analysis, as demonstrated in a study comparing naïve Bayes, SVM, and K-Nearest Neighbor (K-NN) methods with RapidMiner5. The study found that the naïve Bayes method performed well in sentiment analysis, highlighting RapidMiner’s potential for this type of analysis.
Key Features:
Data mining and predictive analytics
GUI editor for building custom processes and models
Data visualization and statistical analysis
Integration with external tools and plugins, such as the MeaningCloud plugin for sentiment analysis
Pricing Model & Free Trial Period:
RapidMiner provides a free trial period in which users can test the software before purchasing a subscription. RapidMiner provides two options for licensing:
1. With Altair Units: This license method provides access to a variety of Altair products, including RapidMiner Studio. Altair Units are more flexible than traditional RapidMiner licenses and require a connection to an on-premise Altair license server.
2. Traditional RapidMiner License: This license method is limited to RapidMiner Studio and requires logging in to the license server or manually inserting the license.
The license specifies the number of logical processors and data limits, which can be viewed via the settings menu. If the data limits are exceeded, users can downsample their data, upgrade their license, or use specific operators to control and preserve the downsampling strategy.
Best For: RapidMiner is best suited for businesses and individuals seeking a powerful data science and mining platform capable of predictive analytics and data visualization. The study comparing naïve Bayes, SVM, and K-NN methods using RapidMiner demonstrates its suitability for sentiment analysis.
User Reviews: RapidMiner is a well-known and widely used data science and data mining tool, with a positive user reputation. [Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making]
14. Repustate
Repustate is a sentiment analysis tool that provides a RESTful API for sentiment scoring and topic-specific sentiment analysis. It uses advanced natural language processing (NLP) and machine learning techniques to extract sentiment from text data. Repustate’s sentiment analysis API can analyze text in multiple languages and supports emoticons, emojis, and internet slang.
Repustate’s sentiment analysis. API scores sentiment on a scale of -1 (negative) to 1 (positive), with 0 representing neutral. It can also conduct topic-based sentiment analysis by narrowing the focus to one or more topics.
Key Features:
RESTful API for sentiment scoring and topic-based sentiment analysis
Utilizes advanced natural language processing (NLP) and machine learning methodologies
Supports multiple languages
Can handle emoticons, emojis, and internet slang
Scores sentiment in the range of -1 (negative) to 1 (positive)
Can perform topic-based sentiment analysis by scoping the sentiment to one or more topics
Pricing Model & Free Trial Period:
Repustate provides flexible pricing, including pay-as-you-go and monthly subscriptions for even greater savings. You are only charged for the data you utilize, with no additional fees. Repustate IQ allows you to set a budget and avoid spending more than you want. Purchase data credits ahead of time and redeem them for social media, news, blogs, surveys, and any data stored in Excel spreadsheets.
Best For: Repustate is ideal for companies and organizations that must analyze large amounts of text data for sentiment and topic-based sentiment analysis. It is appropriate for businesses in a variety of fields, including marketing, customer service, and market research.
User Reviews: Repustate has received positive feedback and ratings from users. Repustate has received high praise from users for its accuracy, ease of use, and flexibility. The tool has been used by a variety of organizations, including Sprout, Nahdi, and other public and private sector clients. Users have also praised Repustate’s developer-friendly API, excellent documentation, and quick and responsive support.
15. Rosette
Rosette Text Analytics’ Sentiment Analyzer identifies emotional hotspots in text about companies, people, and products. It can identify keywords, significant phrases, and unnamed topics, as well as measure text similarity across nine languages. The sentiment analysis tool assigns sentiment scores to individual entities within a larger context.
The platform includes text analytics fundamentals for preparing data for analysis, automatic content classification, and an AI engine for accurate named entity recognition across 21 languages.
Key Features:
Rosette Name Matcher
Rosette Name Translator
Rosette Language Identifier
Rosette Categorizor
Rosette Relationship and Events Extractor
Rosette Topic Extractor
Rosette Base Linguistics
Rosette Entity Extractor and Linker
Rosette Semantic Similarity
Rosette Sentiment Analyzer
Pricing Model & Free Trial Period:
Rosette Text Analytics charges £20,000 per transaction per year. The platform provides a free trial for users to test its features before committing to a subscription.
Best For: Rosette Text Analytics is ideal for businesses that need advanced text analysis, sentiment analysis, and named entity recognition in multiple languages. It is appropriate for various industries, including marketing, customer service, and market research. [Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making]
16. Talkwalker’s Quick Search
Talkwalker’s Quick Search is a social media search engine that offers advanced sentiment analysis capabilities with an average accuracy of 90%. It allows brands to detect consumer sentiment by analyzing attitudes and contextual reactions in social media posts, blog posts, news sites, forums, articles, and review sites. The AI-powered sentiment analysis tool recognizes the true meaning of text, giving brands valuable insights into consumer perceptions and reactions.
The platform works with a variety of applications, such as Google Analytics 360, Twitter/X, Tableau, Meta for Business, and Hootsuite. Talkwalker provides support via email/help desk, knowledge base, phone support, and chat. The platform provides various training options, including documentation, in-person, live online, videos, and webinars.
Talkwalker provides support for multiple languages, such as English, French, German, Italian, Japanese, Portuguese, and Spanish. Talkwalker provides an API for integrating with other applications. The platform is intended for large enterprises, medium-sized businesses, and non-profit organizations.
Talkwalker’s Quick Search employs AI-powered sentiment analysis to accurately categorize sentiment in text data. With up to 90% accuracy, the tool can detect positive, negative, and neutral sentiments in social media conversations, blog posts, news articles, forums, and review websites.
Key Features:
Overview of brand KPIs including engagement, volume, sentiment, demographics, and geographies
Real-time trend tracking for targeted marketing messages
Creation of engaging viral content
Comparison of multiple brands and benchmarking against competitors
Easy and user-friendly interface for deep diving into competitors’ social sphere
Pricing Model & Free Trial Period:
Talkwalker’s pricing model is subscription and quotation-based, with the Listening plan starting at $9,000.00 per year and designed for growing brands and agencies looking for an easy way to get started with social listening.
The platform also provides custom plans at the customer’s request. Talkwalker does not offer a free plan, but it does provide a free trial of its Premium Plans.
Best For: Talkwalker’s Quick Search is ideal for businesses and organizations that want to track and analyze consumer sentiment across social media and other online platforms. It is appropriate for brands that value understanding consumer attitudes, tracking trends, and effectively engaging with their target audience.
Benefits of Sentiment Analysis Tools
Some of the key benefits of Sentiment Analysis Tools inckude:
Speed and Efficiency: AI-powered sentiment analysis allows businesses to quickly analyze large amounts of customer feedback in minutes, a task that would otherwise take hours or even days to complete.
Improved Accuracy: Machine learning algorithms trained on large datasets correctly identify text patterns indicating positive or negative sentiment. The algorithm’s accuracy can be improved further as more data is collected.
Consistency: Unlike humans, AI algorithms use a standardized approach to sentiment analysis that is free of biases and inconsistencies, resulting in reliable and consistent analysis of customer feedback.
Scalability: AI-powered sentiment analysis can handle large volumes of customer feedback without requiring additional resources, making it ideal for businesses that receive a lot of feedback or operate globally.
Valuable Insights and Trends: Scaled analysis provides insights into customer sentiment, allowing businesses to identify areas for improvement, respond to issues in real time, and make data-driven decisions to improve customer satisfaction.
In summary, AI-driven sentiment analysis provides numerous benefits to businesses, such as speed, accuracy, consistency, scalability, and valuable insights, allowing them to efficiently analyze customer feedback, identify trends, and make informed decisions to improve customer satisfaction.
Challenges in implementing Sentiment Analysis Tools
AI-powered sentiment analysis is an effective tool for analyzing customer feedback and forecasting market trends, but it has some limitations. Here are some of the main challenges and limitations of AI in sentiment analysis.
Ambiguity and Context: Because they rely heavily on context, sentiment analysis models may struggle to understand ambiguous language or sarcasm. This can lead to misclassification of sentiment, particularly in informal or colloquial language.
Domain-Specific Knowledge: AI models must be trained on domain-specific data in order to accurately classify sentiment in industry-specific contexts. Without domain-specific knowledge, AI models may struggle to comprehend industry-specific jargon and context.
Bias and Fairness: AI models can inherit biases from training data, resulting in inaccurate sentiment analysis results. This can have a negative impact on businesses, as biased analysis can skew decision-making and perpetuate unfair behaviour.
Data Quality and Availability: AI models rely heavily on high-quality labeled data to train. Obtaining large amounts of labeled data can be difficult, especially in specialized industries or domains.
Ethical Considerations: AI-powered sentiment analysis raises ethical concerns, including privacy and data security. Businesses must ensure that customer data is managed responsibly and that privacy regulations are followed.
Accuracy Limitations: While AI-powered sentiment analysis has the potential for high accuracy, it can still produce incorrect results. The quality of the data used to train the algorithm has a significant impact on its accuracy.
Contextual Understanding: AI algorithms struggle to understand context and nuances in language, which can result in inaccurate sentiment analysis.
Ambiguity: Sentiment analysis is not always simple, and the meaning of a statement may be unclear. This can cause confusion and errors in the analysis, especially when dealing with sarcasm or irony.
Multilingual Analysis: AI-powered sentiment analysis may struggle to accurately analyse text in multiple languages. Different languages have unique nuances, grammar, and expressions that can be difficult for algorithms to interpret accurately.
Privacy Concerns: Another drawback of using AI for sentiment analysis is privacy issues. Text data frequently includes sensitive information, and there are concerns about how it is collected, stored, and used.
Cost: Implementing AI-powered sentiment analysis can be costly, especially for small businesses that may lack the resources to invest in the technology. The cost of collecting and analyzing large amounts of data can be significant.
Understanding these challenges is critical for businesses that want to implement and use sentiment analysis effectively to gain valuable insights into customer sentiment and make data-driven decisions that lead to better products and services. [Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making]
Final Thoughts
I hope you found the suggestions and recommendations in this article useful and informative. Given the growing importance of sentiment analysis and text analytics in today’s digital world, businesses must use these tools to gain valuable insights into their brand reputation, customer sentiment, and market trends.
You can select the tool or platform that best meets your needs and budget from those mentioned in this article. Whether you need a standalone sentiment analysis tool or a comprehensive text analytics platform, there are many options available.
I encourage you to try out these ideas and recommendations in your own business or organization. Using sentiment analysis and text analytics can help you gain a competitive advantage, improve customer satisfaction, and make more informed business decisions.
Please share your experience and journey in the comments section below. I’d love to hear about how these tools and recommendations have benefited you and your business.
Top 16 AI Sentiment Analysis Tools for Data-Driven Decision Making FAQs
What is sentiment analysis and how is it used in business?
Sentiment analysis is a method of analyzing text data to identify and categorize emotions, opinions, and attitudes. In business, it is used to extract insights from customer feedback, social media posts, and other text data sources to improve customer experience, product innovation, and brand reputation management.
What are some examples of businesses using sentiment analysis?
Nike used social media sentiment analysis to gauge public opinion after sponsoring NFL player Colin Kaepernick, a mobile carrier used customer support sentiment analysis to improve its services, and a B2C app developer used social media sentiment analysis to track new trends and identify when to become a part of the conversation.
What are the benefits of sentiment analysis for businesses?
Sentiment analysis can help businesses gain insights into customer opinions and attitudes, improve customer experience and satisfaction, and make data-driven decisions based on reliable awareness of how they are perceived across channels.
What are some popular sentiment analysis tools?
Talkwalker, Graphext, Repustate, Brand24, and Meltwater are some popular sentiment analysis tools.
How accurate is sentiment analysis?
Sentiment analysis can achieve up to 90% accuracy in categorizing sentiment of brand mentions as positive, negative, or neutral.
What is the difference between social media monitoring and sentiment analysis?
Social media monitoring focuses specifically on social media monitoring and analysis, while sentiment analysis emphasizes visualizing relationships between entities and keywords extracted from text.
How does sentiment analysis help with brand reputation management?
Sentiment analysis helps businesses monitor and analyze sentiment in external corporate statements, ensuring that every word and phrase does not affect the brand negatively.
What is a sentiment analysis API?
A sentiment analysis API is an algorithm that extracts sentiment from any kind of data uploaded as a file, text string, or text/video URL.
How does sentiment analysis benefit competitor research?
Sentiment analysis benefits competitor research by making it more thorough and actionable, providing insights into how consumers perceive competitors.
How does sentiment analysis help with customer feedback analysis?
Sentiment analysis helps businesses analyze the sentiment behind answers to open-ended survey questions, giving valuable qualitative insights into customer feedback.