Top 7 Open Source Sentiment Analysis Tools in 2025

Text analytics is estimated to exceed a global market value of US$ 56 billion by 2029.¹ Sentiment analysis has gained worldwide momentum as one of the text analytics applications. Businesses that have not implemented sentiment analysis may feel an urge to find out the best tools and use cases for benefiting from this technology.

Explore the top open source sentiment analysis tools and no-code solutions for businesses looking to pilot sentiment analysis at no cost:

Top open-source sentiment analysis coding packages:

1. spaCy

The highest ranking sentiment analysis package on Github is spaCy, with 30K stars in Natural Language Processing.² It supports more than 60 languages and has very extensive documentation. Built in mostly in Python, it is a combination of 6 different programming languages. This platform provides extensive community content to help out developers at any level, from beginners to advanced.³

• Advantages: Suitable for data scientists due to its rich documentation and active community.
• Use Case: Ideal for advanced sentiment analysis tasks requiring high customization.

2. NLP.JS

A high-ranking sentiment analysis package with 6K stars as of 2024 on Github and an alternative for JavaScript developers is Nlp.js.⁴ This package is developed by Axa Insurance Group and shared openly.

As the most commonly used programming language for web scraping, this package is built in JavaScript and has extensive documentation and examples, specifically useful for beginner developers in sentiment analysis. This package shines by supporting 40 different languages natively.

• Advantages: Great for real-time sentiment analysis and projects focused on social media data.
• Use Case: Best for social media monitoring or applications requiring multi-language support.

3. Pattern

Another high-ranking sentiment analysis package on Github with 8.2k stars as of 2022 is Pattern, mainly in Python.⁵ Compared to spaCy, this package provides data collection options via web scrapers or integrating APIs and applying sentiment analysis on collected data as a comprehensive solution.

There are more than 50 examples provided in the package, which can be a one-stop-shop solution for technical teams that are already experienced in Python.

• Advantages: Comprehensive tool for sentiment analysis projects with built-in web scrapers.
• Use Case: Suitable for teams experienced in Python and requiring text analysis and emotion analysis.

4. VADER

VADER (Valence Aware Dictionary and sEntiment Reasoner), with 4.5K Github stars, is a widely recognized sentiment analysis tool, particularly for social media sentiment analysis and opinion mining.⁶ It stands out for its lexicon and rule-based approach to analyzing sentiments expressed in online conversations, making it highly suitable for assessing the emotional tone of social media data.

Unlike complex machine learning algorithms, VADER uses a predefined sentiment lexicon tailored to social media language, incorporating emoticons, acronyms, and slang commonly found in online text. Its simplicity and effectiveness make it an excellent choice for both data scientists and market researchers aiming to extract actionable insights from large volumes of text data.

• Advantages: Excellent for analyzing informal text from social media platforms.
• Use Case: Ideal for analyzing sentiment in online conversations and social media posts.

5. TextBlob

TextBlob is another popular sentiment analysis tool, with 9K Github stars, widely used for processing textual data, built specifically in Python.⁷ It provides a simple and user-friendly API for performing a variety of natural language processing tasks, including sentiment analysis, part-of-speech tagging, and noun phrase extraction.

TextBlob is especially valued for its accessibility to beginners and researchers who need an intuitive tool for analyzing sentiment without extensive knowledge of machine learning models. With features like sentiment classification, parsing, and API integration, TextBlob offers a versatile framework for tasks such as customer feedback analysis, real-time sentiment analysis, and social media monitoring

• Advantages: Easy-to-use API for beginners and smaller sentiment analysis projects.
• Use Case: Great for businesses new to sentiment analysis tools.

Top low-code or no-code open-source sentiment analysis tools:

1. MeaningCloud

MeaningCloud is used by multiple big corporations for sentiment analysis and offers a free tier that may be available for the volume of your sentiment analysis needs.⁸

This free tier also supports API integration, which may help automate your text analysis process. Most paid sentiment analysis tools online will offer you a limited-time free trial with their full functionalities. MeaningCloud is different by providing a continuous free service with limited volume and capability, which may still be sufficient for your business needs.

• Advantages: Allows businesses to try sentiment analysis tasks without upfront costs.
• Use Case: Best for businesses looking to pilot customer feedback analysis.

2. Social Searcher:

Social Searcher specializes in social media sentiment analysis and has experience working with big corporations. Their dashboard view is particularly helpful to compare different platforms and have a crisp understanding of the overall picture of a specific keyword, which can be especially useful for marketing use cases such as tracking a hashtag of a recently launched campaign.

Social Searcher offers real-time searches for free, and the dashboard is available in their paid plan. However, compared to other paid sentiment analysis tools in the market, the paid plan in 2022 seems to start at a reasonable rate.⁹

• Advantages: Insights into sentiments expressed across multiple languages.
• Use Cases: Perfect for market research and monitoring various social media platforms.

How open source platforms are used for sentiment analysis?

Open-source platforms are indispensable for analyzing textual data, which is the final step in a sentiment analysis project. These platforms typically include sentiment classifiers capable of assessing text data to determine whether the sentiments expressed are positive, negative, or neutral, assigning an overall sentiment score to each input.

These tools are built upon natural language processing (NLP) and often leverage machine learning algorithms or deep learning models. Key considerations for businesses evaluating these platforms include their accuracy, multi-language support, and integration capabilities for various data sources.

Performing sentiment analysis involves three main steps:

1. Data Acquisition: Collecting textual data from various data sources, such as social media platforms or customer reviews.
2. Model Selection: Choosing an appropriate sentiment analysis model, which may include pre-trained models or custom models.
3. Analysis: Using a sentiment analysis tool to process and classify the data into positive sentiments, negative sentiments, or neutral sentiments.

Open source platforms primarily facilitate the third step, offering tools to analyze text data and generate sentiment classification. These platforms include robust text classifiers, machine learning algorithms, and APIs for integration with existing systems.

Key concerns when choosing open source solutions include accuracy, multi-language support, and the availability of extensive documentation.

Pros and cons of open source sentiment analysis platforms

Pros

• Cost-effective for small businesses and sentiment analysis projects.
• Active online communities foster innovation and continuous improvements.
• Easy integration with existing data sources for rapid implementation.

Cons

• Risk of not being compliant with security requirements, especially for big corporations
• Limited support for specific troubleshooting needs given the lack of a formal support layer
• Lack of sustainability due to multiple cases such as solution discontinuity, version upgrades, or changes in licenses and fees
• Mostly dependent on data being collected and processed separately
• Risks of discontinuation or compatibility issues with deep learning models.

For more on sentiment analysis and open source solutions:

To explore more on open source automation solutions and NLP applications, read our articles:

• Natural Language Understanding: In-Depth Guide
• Top 6 Open Source RPA Providers
• Quick guide to data collection

External Links

• 1. Text Analytics Market - Size, Growth & Industry Forecast.
• 2. spaCy · Industrial-strength Natural Language Processing in Python.
• 3. Bevor Sie zu YouTube weitergehen.
• 4. AXA · GitHub.
• 5. Computational Linguistics Research Group · GitHub.
• 6. cjhutto (C.J. Hutto) · GitHub.
• 7. sloria (Steven Loria) · GitHub.
• 8. Pricing. MeaningCloud. Accessed: November/18/2024.
• 9. Pricing. Social Searcher. Accessed: November/18/2024.