AIMultiple ResearchAIMultiple Research

Top 5 Open Source Sentiment Analysis Tools in 2024

In this article, we will introduce the top open source and no-code sentiment analysis tools and coding packages for businesses to try and run pilot sentiment analysis at no cost to determine the business value this method can bring them.

Text analytics is estimated to reach a global market value of US$ 4.84 billion by 2026. As seen in Figure1, 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.

Figure 1. Sentiment analysis popularity rise

The trend chart shows a rising trend of the popularity of sentiment analysis on google search.

How open source platforms are used for sentiment analysis?

In our in-depth guide for sentiment analysis, we laid out the three main steps of sentiment analysis: data acquisition, model selection, and analysis. Open source platforms are tools that mainly offer the third step, which is analyzing already collected data. They possess a text classifier, which tags the words or groups of words in a text as negative, positive or neutral and gives an overall sentiment score to the text.

Sentiment analysis frameworks are an application of natural language processing, and share similar concerns with the bigger NLP space. The tool’s accuracy, performance across different languages, and robustness to connect to your data source are all important and dependent on how actively the open source solution is supported.

Top open-source sentiment analysis coding packages:

1. spaCy

The highest ranking sentiment analysis package on Github is spaCy, with 22.5k stars in Natural Language Processing. 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.


A high-ranking sentiment analysis package with 4.8k stars as of 2022 on Github and an alternative for JavaScript developers is Nlp.js. 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.

3. Pattern

Another high-ranking sentiment analysis package on Github with 8.2k stars as of 2022 is Pattern, mainly in Python. 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.

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. 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.

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.

Pros and cons of open source sentiment analysis platforms


  • Mostly free or at minimal cost, especially for companies that have developer teams
  • Fast to try for a business case or run a pilot with
  • Open to innovation and new features through online communities


  • 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


Bright Data’s automated web data collector scrapes the web from targeted websites and delivers the data in a clean and processed format, readily available for any existing sentiment analysis tools your business leverages. They also provide sample datasets for users who do not have a preference on the targeted websites for data collection.

Source: Bright Data

For more on sentiment analysis and open source solutions:

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

For guidance in choosing the right tool, check out data-driven lists of web scrapers and sentiment analysis services, and feel free to reach out to us:

Find the Right Vendors

This article was drafted by former AIMultiple industry analyst Bengüsu Özcan.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
Follow on

Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

To stay up-to-date on B2B tech & accelerate your enterprise:

Follow on

Next to Read


Your email address will not be published. All fields are required.