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Discover the 20 Best Sentiment Analysis Tools in 2024

Abhishek Punyani
January 10, 2024
10
 mins read

Last modified on

Sentiment analysis tools have become crucial in today's data-driven world. Businesses and organizations rely on these tools to understand public opinion and analyze speech patterns, customer feedback, and market trends. In 2024, the landscape of sentiment analysis tools is evolving, offering a variety of options to cater to different needs. 

In this blog post, we will explore the top 20 sentiment analysis tools available in the year 2024.

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What is Sentiment Analysis?

Sentiment analysis is a tricky thing to understand, but Convin makes it easy to understand and utilize
Sentiment analysis is a tricky thing to understand, but Convin makes it easy to understand and utilize

Sentiment Analysis, also known as sentimental analysis, sentiment analytics, or sentiment analysis tools, is a natural language processing (NLP) technique used to determine and understand the emotional tone, attitude, or sentiment expressed in a piece of text, speech, or other forms of communication. The goal of sentiment analysis is to identify whether the sentiment expressed in the text is positive, negative, or neutral.

Some of the key aspects and features related to sentiment analysis include.

1. Sentiment Classification

Sentiment analysis tools classify text into categories like positive, negative, or neutral, helping organizations understand public opinion and customer feedback.

2. Keyword Extraction

Sentiment analysis tools often extract keywords and phrases that contribute to the overall sentiment, allowing users to identify the key drivers behind the sentiment.

3. Sentiment Score 

Tools may assign a sentiment score or a numerical value to indicate the strength of the sentiment, helping quantify the emotional tone.

4. Customization

Many sentiment analysis tools allow users to customize sentiment categories and dictionaries to align with their specific domain or industry.

5. Real-time Analysis

Some tools offer real-time sentiment analysis, enabling businesses to monitor sentiment as it evolves and respond promptly to changes in public opinion.

6. Visualizations

Sentiment analysis tools often provide visualizations, such as graphs and charts, to help users understand sentiment trends over time.

Suggested Read:- Learn about the best practices of Sentiment Analysis.

Sentiment analysis is a valuable technique that leverages sentiment analysis tools to extract meaningful insights from text data by discerning the sentiment or emotional tone conveyed in the text. These tools are widely used across industries to gain a better understanding of customer opinions, market trends, and public sentiment, ultimately aiding in decision-making and strategy formulation.

Now that we've established a clear understanding of what sentiment analysis is let's delve into the top 20 sentiment analysis tools and software of 2024.

Let’s Explore the Top 20 Sentiment Analysis Tools

In the ever-evolving landscape of sentiment analysis software, businesses and individuals alike must stay updated on the latest and most effective solutions. 

These tools play a pivotal role in understanding and gauging sentiments, opinions, and emotions expressed in text, enabling organizations to make data-driven decisions, improve customer experiences, and gain a competitive edge. 

Let's dive deeper into each of the 20 sentiment analysis tools, including MonkeyLearn, Lexalytics, Convin, and more, and explore their key features and capabilities.

1. MonkeyLearn

  • MonkeyLearn is a cloud-based text analysis platform that offers sentiment analysis. It leverages the capabilities of machine learning and natural language processing (NLP) to perform sentiment analysis and extract valuable insights from textual data.
  • Key Features: Customizable sentiment models, integration with various platforms like Zapier and Google Sheets, and support for multiple languages.

2. Lexalytics

  • Lexalytics is a prominent player in the field of text analytics and sentiment analysis. The Lexalytics Sentiment Analysis API is a powerful tool designed to extract sentiment and emotions from large volumes of text data rapidly and accurately. 
  • Key Features: Multi-language support, detailed sentiment scoring, and industry-specific solutions for market research, social media monitoring, and more.

3. Convin.ai

  • Certainly! Convin offers a user-friendly sentiment analysis tool that harnesses the power of artificial intelligence (AI) and machine learning (ML) to provide valuable insights from customer feedback and online discussions. Convin's sentiment analysis tool relies on advanced AI and ML algorithms. These algorithms have been trained on large datasets of text to recognize patterns and context in language. This allows the tool to provide accurate sentiment analysis results even in cases with nuanced or complex language.

4. Tone Analyzer by IBM Watson

  • IBM's Tone Analyzer is a powerful natural language processing (NLP) tool designed to analyze text and detect emotional tones expressed within it. This tool leverages advanced machine learning algorithms to identify and categorize a variety of emotional tones, such as joy, anger, sadness, and more.
  • Key Features: API integration, tone detection, and sentiment scoring.

5. VADER (Valence Aware Dictionary and Sentiment Reasoner)

  • VADER, which stands for Valence Aware Dictionary and Sentiment Reasoner, is a popular open-source sentiment analysis tool used to assess sentiment in text data. VADER is known for its simplicity and effectiveness in determining sentiment by relying on a predefined lexicon and a rule-based approach.
  • Key Features: Effective for social media sentiment analysis, sentiment polarity scoring, and quick setup.

6. TextBlob

  • TextBlob is a user-friendly Python library designed for various text analysis tasks, and one of its notable capabilities is sentiment analysis. It simplifies the process of analyzing text to determine the sentiment or emotional tone within it.
  • Key Features: Ease of use, support for English and non-English text, and extensibility for custom analysis.

7. Semantria by Lexalytics

  • Semantria is a sophisticated text analysis platform that goes beyond simple sentiment analysis. It combines sentiment analysis with text analytics to provide a comprehensive understanding of textual data. Semantria employs natural language processing (NLP) techniques to analyze text and determine the sentiment expressed within it. It can categorize text as positive, negative, or neutral based on the emotional tone and language used.
  • Key Features: Entity recognition, customizable models, and integration with popular data analytics platforms.

8. RapidMiner

  • RapidMiner is a comprehensive data science platform that offers a wide range of data analytics tools, including sentiment analysis, to help businesses and data professionals extract valuable insights from their data. What sets RapidMiner apart is its user-friendly and intuitive visual interface, making it accessible even to non-technical users.
  • Key Features: Drag-and-drop interface, predictive analytics, and support for various data sources.

9. Google Cloud Natural Language

  • Google's sentiment analysis tool, which is part of its cloud services, and explore how it offers accurate sentiment analysis while seamlessly integrating with other Google Cloud tools.
  • Key Features: Cloud-based, easy integration, and support for multiple languages.

10. Aylien

  • Aylien's sentiment analysis API is a powerful tool that provides businesses and developers with the ability to analyze text data for sentiment, along with additional features such as entity recognition and text classification.
  • Key Features: Multilingual support, entity recognition, and text classification.

11. Clarabridge

  • Clarabridge is a robust customer experience management platform designed to provide businesses with valuable insights into their customer feedback and sentiment trends. This platform incorporates advanced sentiment analysis features as part of its toolkit
  • Key Features: Omnichannel data collection, root cause analysis, and actionable insights.

12. Brandwatch

  • Brandwatch is a powerful social listening tool designed to help businesses monitor their online presence and reputation. It goes beyond just tracking brand mentions on various social media platforms; it also includes built-in sentiment analysis features.
  • Key Features: Real-time monitoring, influencer identification, and competitive analysis.

13. Hootsuite

  • Hootsuite recognizes the importance of understanding how the public perceives a brand, product, or service on social media. Sentiment analysis, in this context, involves the automated extraction of emotions, opinions, and attitudes expressed in social media posts, comments, and mentions.
  • Key Features: Social media scheduling, analytics dashboards, and sentiment monitoring.

14. IBM Watson Discovery

  • IBM Watson Discovery is a powerful cognitive computing platform that combines several advanced capabilities, including sentiment analysis, document retrieval, and insights generation. This platform is designed to assist businesses in efficiently managing and extracting valuable information from their extensive collections of documents, such as reports, articles, customer feedback, or research papers.
  • Key Features: Document indexing, insights generation, and customizable dashboards.

15. SentiStrength

  • SentiStrength is a specialized sentiment analysis tool designed primarily for analyzing social media data, making it particularly useful for understanding the sentiments expressed on platforms like Twitter, Facebook, Instagram, and more. What sets SentiStrength apart is its ability to provide insights into both positive and negative sentiments within this specific context.
  • Key Features: Social media sentiment analysis, emotion detection, and sentiment scoring.

16. Brand24

  • Brand24 is a comprehensive social media monitoring tool designed to help businesses keep a close eye on their online presence and reputation. One of its key features is its sentiment analysis capabilities, which allow companies to gain deeper insights into how their brand is perceived in the digital world.
  • Key Features: Real-time alerts, sentiment tracking, and influencer identification.

17. Talkwalker

  • Talkwalker is a robust and multifaceted social media monitoring and analytics platform designed to help businesses and organizations gain valuable insights from online conversations. One of its key strengths lies in its ability to perform sentiment analysis in real-time, offering users a comprehensive view of brand sentiment across various social media platforms and online channels.
  • Key Features: Social media listening, sentiment trend analysis, and competitive benchmarking.

18. NLP API by Algorithmia

  • Algorithmia's NLP API is a versatile and powerful tool that offers a range of natural language processing (NLP) capabilities, including sentiment analysis, text classification, and content recommendation.
  • Key Features: Text classification, content recommendation, and developer-friendly API.

19. Receptiviti

  • Receptivity is a specialized platform that harnesses the power of Natural Language Processing (NLP) and machine learning to provide comprehensive insights into individuals' personalities and emotions through textual data analysis. It is particularly useful for businesses and organizations looking to gain a deeper understanding of their customers, clients, or target audiences.
  • Key Features: Personality profiling, emotion detection, and sentiment analysis.

20. SAP HANA

  • SAP HANA combines in-memory computing with sentiment analysis for real-time insights. It's suitable for large enterprises looking to integrate sentiment analysis into their business processes.
  • Key Features: In-memory processing, real-time analytics, and integration with SAP's ecosystem.

These 20 sentiment analysis tools offer a wide range of capabilities to suit various business needs, from social media monitoring to in-depth text analytics. Whether you're a data scientist, marketer, or business owner, you can find a tool that fits your requirements and helps you gain valuable insights from textual data. Convin, in particular, excels in providing a user-friendly sentiment analysis experience, making it a top choice for organizations seeking actionable insights from customer feedback and online discussions.

Curious? Get on a call with us today to see firsthand how our sentiment analysis tool can help!

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FAQ

1. Which tool is used for sentiment analysis?

Popular tools for sentiment analysis include TextBlob, NLTK, Convin.ai, VADER, and spaCy, which offer Python libraries and pre-trained models.

2. Can I use ChatGPT for sentiment analysis?

While ChatGPT can process and analyze text, it's primarily a language model for generating human-like text and may require additional code to perform sentiment analysis effectively.

3. What is the Python tool for sentiment analysis?

Python's NLTK, TextBlob, and the Hugging Face Transformers library are commonly used for sentiment analysis tasks.

4. What is the best model for sentiment analysis?

The choice of the best model for sentiment analysis depends on the specific dataset and task, but models like BERT, GPT-3, and LSTM-based networks have shown strong performance.

5. What is NLP in sentiment analysis?

In sentiment analysis, NLP (Natural Language Processing) refers to the use of computational techniques to understand and analyze text data for sentiment classification.

6. What is the NLP process for sentiment analysis?

The NLP process for sentiment analysis typically involves data preprocessing, feature extraction, model training, and sentiment prediction using machine learning or deep learning algorithms.

7. What are the four main steps of sentiment analysis?

The four main steps of sentiment analysis are 

   a. Text Preprocessing: Cleaning and preparing the text data.

   b. Feature Extraction: Converting text into numerical features.

   c. Model Training: Training a machine learning or deep learning model on labeled data.

   d. Sentiment Prediction: Applying the trained model to classify text into sentiment categories (positive, negative, neutral).

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