{ "source_file": "0930", "source_platform": "Medium", "author": "Petr Koráb", "title_listed": "Bigram Word Cloud Animates Your Data Stories", "keywords": "", "domain": "文本处理", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/bigram-word-cloud-animates-your-data-stories-753649d5cf4d/", "publish_date": "2024-05-08", "corpus_stage": "", "notes": "", "id": "7b0a0bb7db74", "collected_at": "2026-10-02T03:44:07", "title": "Bigram Word Cloud Animates Your Data Stories | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. 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Image by Author.](../images/000.png)\nSource: [AnimatedWordCloud ](https://pypi.org/project/AnimatedWordCloud)library. Image by Author.\n**Animated word cloud** displays n-gram frequencies (words and consequent words in a text corpus) over time as a sequence of images in a video file. **** I** t**gives greater importance to words that appear more frequently in a source text, but it scales the dataset to work with different datasets. The original visualization method uses the intuitive logic of classic**[**word clouds**](https://www.datacamp.com/tutorial/wordcloud-python)**and adds a time dimension to the graph. It has been designed to explore text datasets collected over multiple periods (referred to as** __ \"time-series text data\"**).\n[Michael Kane](https://github.com/thisIsMikeKane) developed the core framework for animating word frequencies, and the _AnimatedWordCloud_ (AWC) library implemented the visualization method into practice. __ The new release brings important updates:\n * _**data scaling**_ : it now works better with text datasets of different sizes and word frequencies\n * extending the _n _gram_ parameter ( = 2) to generate _**bigram word clouds**_\n * _**efficiency improvements**_ (saving now 220 frames for each period, improved Y axis, etc.).\n\n\nThis article will give a glimpse of how to create a bigram-animated word cloud from a text dataset of product reviews. Read my [main framework](https://medium.com/python-in-plain-english/animated-word-cloud-a-novel-way-for-the-visualization-of-word-frequencies-6505418acbb3) and [unigram](https://medium.com/p/1889fdeb97b8) tutorials to learn more about animated word clouds.\n## 2. Use cases\nEarlier releases of the AWC were already used to tell the stories of:\n * **Economic research trends** : research published in A-class Economics journals since 1900.\n\n\nPlease accept cookies to access this content\n * **European Central Bank (ECB) communication** : public speeches of economists and researchers of the ECB since the Eurozone launch.\n\n\nPlease accept cookies to access this content\n## 3. Bigram Animated Word Cloud\n### 3.1. How to install the library\nInstall the library in a new **Python 3.8** virtual environment to avoid dependence conflicts. It relies on Python 3.8 because of its visualization [requirements](https://github.com/PetrKorab/Animated-Word-Cloud/blob/main/requirements.txt). To install using pip, use:\npython\n\n```\npip install AnimatedWordCloud\n```\n\n### 3.2. Data\nFor the AWC bigram exercise, we'll use the [**Amazon Dog Food Reviews**](https://huggingface.co/datasets/NiyatiC/amazon_food_reviews) dataset from the **Hugging Face** , released under the [Apache-2.0 license](https://github.com/huggingface/datasets/blob/main/LICENSE). The example subset covers 305 reviews from 2020-01-01 to 2021-04-16 and can be downloaded from [here](https://github.com/PetrKorab/Animated-Word-Cloud/tree/main/examples).\nHere is what the data looks like:\n![Image 3. Amazon dog food reviews dataset](../images/001.png)\nImage 3. Amazon dog food reviews dataset\n### 3.3. Product review analysis\n_Product reviews_ are very useful sources of information for companies because they contain qualitative information about the customers' satisfaction with products (or services).\nText mining analysis can reveal bigrams with **positive and negative meanings** , and also bigrams that indicate important **factors of product satisfaction**. Read my previous TDS article about [customer satisfaction measurement with n-gram and sentiment analysis](https://towardsdatascience.com/customer-satisfaction-measurement-with-n-gram-and-sentiment-analysis-547e291c13a6) to learn more.\n> Finding a high frequency of bigrams with positive meanings, e.g., _\"highly satisfied\", \"fast delivery,\"_ or _\"great service\"_ and bigrams with negative meanings, e.g., _\"poor quality\"_ or _\"damaged products\"_ might indicate import problems hampering the company's growth.\nNow, let's generate a Bigram animated word cloud for the text reviews in **monthly frequency** , cleaning data from **English stopwords** and also cutting out other **redundant strings** (_'years old', 'year old', 'every day', 'Amazon'_):\nThe code creates 200 PNG word cloud images per period. The frames are stored in the working directory in the newly created _.post _processing/frames_ folder.\nNext, download the _ffmpeg_ folder and the _frames2video.bat_ file from [here](https://github.com/PetrKorab/Animated-Word-Cloud) and place them into the _postprocessing_ folder. Running the _frames2video.bat_ generates a _wordSwarmOut.mp4_ video file. The video is on my YouTube:\nPlease accept cookies to access this content\n> The brand associated with the reviews appears to be Blue Buffalo. We can see large frequencies of bigrams with **positive meanings** - \"**dog loves** \", \"**dog likes** \", and \"**love food** \" until Oct 2020. The associated qualities are price (\"**good price** \") and product quality (\"**life protection** \").\n> Since Dec 2020, there seem to be **negative health effects** of the products (\"**bloody vomit** \", \"**vomit stool,** \" \"**heart disease** ,\" \"**vomiting diarrhea** \"). In this situation, the company should check the production line and the delivery process to find out the reasons.\n## Conclusions\n_Animated Word Cloud_ is an original text visualization method for datasets with time variability. The library is a result of applied text-mining research. Originally developed by[ Michael Kane](https://github.com/thisIsMikeKane/WordSwarm), the Python implementation relies on the preprocessing of the [Arabica ](https://pypi.org/project/arabica/)library, which is now forthcoming in the _Journal of Open Source Software_ [(Koráb, Poměnková, 2024)](https://zenodo.org/records/10866697).\nThe library is still under development. Let's mention several drawbacks of using the AWC for time-series text visualization:\n * Every video generation is a **random visualization exercise**. This means that:\n\n\n_Color, word positions, and other parameters are_ _**generated for each trial**_ _. In certain trials, the words might overlap, lowering the quality of the data story. Therefore, it is helpful to create a couple of videos, compare the visuals, and select the nicest one._\n * The **video keeps relative differences in n-gram frequencies over the whole time period.** However, it is scaled by several constants to work with different types of text datasets.\n\n\n_The library has been tested and optimized to work with datasets of various sizes, periods, and word frequencies, but it is still under development. If you find any visualization issues that hamper the video quality, please_[ _**open an issue here**_](https://github.com/PetrKorab/Animated-Word-Cloud/issues) _**to get it improved.**_\n * The **skip parameter cuts out raw strings but not tokens.** Consider adding another preprocessing step if the text dataset is just too noisy.\n\n\n_Skip removes strings as they are and does not consider tokenization of the dataset. Removing strings such as \"Mar\" might lead to a bias because a bigram \"Mario Braghi\" then might become \"io Braghi\". In practical situations, it is useful to use the skip parameter to remove complicated strings such as:_`_skip = ['https://1236578995.org/@789', '@JaayZen/']_`\n* * *\n_If you enjoy my work, you can invite me_[ _for coffee_](https://www.buymeacoffee.com/petrkorab) _and support my writing. You can also subscribe to my_[ _email list_](https://medium.com/subscribe/@petrkorab) _to get notified about my new articles. Thanks!_\n## References\n[1] Koráb, & Poměnková. (2024). Arabica: A Python package for exploratory analysis of text data. Journal of Open Source Software, 9(97), 6186. \n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](../images/body-9e78938b5e8b0a70213c.jpeg)\nWritten By\n## [Petr Koráb](https://towardsdatascience.com/author/petrkorab/)\n[See all from Petr Koráb](https://towardsdatascience.com/author/petrkorab/)\n[Animated Word Cloud](https://towardsdatascience.com/tag/animated-word-cloud/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Python](https://towardsdatascience.com/tag/python/)[Text Mining](https://towardsdatascience.com/tag/text-mining/)[Word Cloud](https://towardsdatascience.com/tag/word-cloud/)\nShare This Article\n * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fbigram-word-cloud-animates-your-data-stories-753649d5cf4d%2F&title=Bigram%20Word%20Cloud%20Animates%20Your%20Data%20Stories)\n * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fbigram-word-cloud-animates-your-data-stories-753649d5cf4d%2F&title=Bigram%20Word%20Cloud%20Animates%20Your%20Data%20Stories)\n * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fbigram-word-cloud-animates-your-data-stories-753649d5cf4d%2F&text=Bigram%20Word%20Cloud%20Animates%20Your%20Data%20Stories)\n\n\nTowards Data Science is a community publication. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Bigram Word Cloud Animates Your Data Stories\nHands-on tutorial explaining how to create an Animated Word Cloud of bigram frequencies to display a text dataset in an MP4 video\n[Petr Koráb](https://towardsdatascience.com/author/petrkorab/)\nMay 8, 20245 min read\n### Hands-on tutorial explaining how to create an _Animated_ _Word_ _Cloud_ of bigram frequencies to display a text dataset in an MP4 video\n![Source: AnimatedWordCloud library. Image by Author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/1jH2fzfI6NCWzFMTP4qzOCQ.png)\nSource: [AnimatedWordCloud ](https://pypi.org/project/AnimatedWordCloud)library. Image by Author.\n**Animated word cloud** displays n-gram frequencies (words and consequent words in a text corpus) over time as a sequence of images in a video file. **** I** t**gives greater importance to words that appear more frequently in a source text, but it scales the dataset to work with different datasets. The original visualization method uses the intuitive logic of classic**[**word clouds**](https://www.datacamp.com/tutorial/wordcloud-python)**and adds a time dimension to the graph. It has been designed to explore text datasets collected over multiple periods (referred to as** __ \"time-series text data\"**).\n[Michael Kane](https://github.com/thisIsMikeKane) developed the core framework for animating word frequencies, and the _AnimatedWordCloud_ (AWC) library implemented the visualization method into practice. __ The new release brings important updates:\n * _**data scaling**_ : it now works better with text datasets of different sizes and word frequencies\n * extending the _n _gram_ parameter ( = 2) to generate _**bigram word clouds**_\n * _**efficiency improvements**_ (saving now 220 frames for each period, improved Y axis, etc.).\n\n\nThis article will give a glimpse of how to create a bigram-animated word cloud from a text dataset of product reviews. Read my [main framework](https://medium.com/python-in-plain-english/animated-word-cloud-a-novel-way-for-the-visualization-of-word-frequencies-6505418acbb3) and [unigram](https://medium.com/p/1889fdeb97b8) tutorials to learn more about animated word clouds.\n## 2. Use cases\nEarlier releases of the AWC were already used to tell the stories of:\n * **Economic research trends** : research published in A-class Economics journals since 1900.\n\n\nPlease accept cookies to access this content\n * **European Central Bank (ECB) communication** : public speeches of economists and researchers of the ECB since the Eurozone launch.\n\n\nPlease accept cookies to access this content\n## 3. Bigram Animated Word Cloud\n### 3.1. How to install the library\nInstall the library in a new **Python 3.8** virtual environment to avoid dependence conflicts. It relies on Python 3.8 because of its visualization [requirements](https://github.com/PetrKorab/Animated-Word-Cloud/blob/main/requirements.txt). To install using pip, use:\npython\n\n```\npip install AnimatedWordCloud\n```\n\n### 3.2. Data\nFor the AWC bigram exercise, we'll use the [**Amazon Dog Food Reviews**](https://huggingface.co/datasets/NiyatiC/amazon_food_reviews) dataset from the **Hugging Face** , released under the [Apache-2.0 license](https://github.com/huggingface/datasets/blob/main/LICENSE). The example subset covers 305 reviews from 2020-01-01 to 2021-04-16 and can be downloaded from [here](https://github.com/PetrKorab/Animated-Word-Cloud/tree/main/examples).\nHere is what the data looks like:\n![Image 3. Amazon dog food reviews dataset](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/1VplxO9v_u1V-cvHPn2TjhA.png)\nImage 3. Amazon dog food reviews dataset\n### 3.3. Product review analysis\n_Product reviews_ are very useful sources of information for companies because they contain qualitative information about the customers' satisfaction with products (or services).\nText mining analysis can reveal bigrams with **positive and negative meanings** , and also bigrams that indicate important **factors of product satisfaction**. Read my previous TDS article about [customer satisfaction measurement with n-gram and sentiment analysis](https://towardsdatascience.com/customer-satisfaction-measurement-with-n-gram-and-sentiment-analysis-547e291c13a6) to learn more.\n> Finding a high frequency of bigrams with positive meanings, e.g., _\"highly satisfied\", \"fast delivery,\"_ or _\"great service\"_ and bigrams with negative meanings, e.g., _\"poor quality\"_ or _\"damaged products\"_ might indicate import problems hampering the company's growth.\nNow, let's generate a Bigram animated word cloud for the text reviews in **monthly frequency** , cleaning data from **English stopwords** and also cutting out other **redundant strings** (_'years old', 'year old', 'every day', 'Amazon'_):\nThe code creates 200 PNG word cloud images per period. The frames are stored in the working directory in the newly created _.post _processing/frames_ folder.\nNext, download the _ffmpeg_ folder and the _frames2video.bat_ file from [here](https://github.com/PetrKorab/Animated-Word-Cloud) and place them into the _postprocessing_ folder. Running the _frames2video.bat_ generates a _wordSwarmOut.mp4_ video file. The video is on my YouTube:\nPlease accept cookies to access this content\n> The brand associated with the reviews appears to be Blue Buffalo. We can see large frequencies of bigrams with **positive meanings** - \"**dog loves** \", \"**dog likes** \", and \"**love food** \" until Oct 2020. The associated qualities are price (\"**good price** \") and product quality (\"**life protection** \").\n> Since Dec 2020, there seem to be **negative health effects** of the products (\"**bloody vomit** \", \"**vomit stool,** \" \"**heart disease** ,\" \"**vomiting diarrhea** \"). In this situation, the company should check the production line and the delivery process to find out the reasons.\n## Conclusions\n_Animated Word Cloud_ is an original text visualization method for datasets with time variability. The library is a result of applied text-mining research. Originally developed by[ Michael Kane](https://github.com/thisIsMikeKane/WordSwarm), the Python implementation relies on the preprocessing of the [Arabica ](https://pypi.org/project/arabica/)library, which is now forthcoming in the _Journal of Open Source Software_ [(Koráb, Poměnková, 2024)](https://zenodo.org/records/10866697).\nThe library is still under development. Let's mention several drawbacks of using the AWC for time-series text visualization:\n * Every video generation is a **random visualization exercise**. This means that:\n\n\n_Color, word positions, and other parameters are_ _**generated for each trial**_ _. In certain trials, the words might overlap, lowering the quality of the data story. Therefore, it is helpful to create a couple of videos, compare the visuals, and select the nicest one._\n * The **video keeps relative differences in n-gram frequencies over the whole time period.** However, it is scaled by several constants to work with different types of text datasets.\n\n\n_The library has been tested and optimized to work with datasets of various sizes, periods, and word frequencies, but it is still under development. If you find any visualization issues that hamper the video quality, please_[ _**open an issue here**_](https://github.com/PetrKorab/Animated-Word-Cloud/issues) _**to get it improved.**_\n * The **skip parameter cuts out raw strings but not tokens.** Consider adding another preprocessing step if the text dataset is just too noisy.\n\n\n_Skip removes strings as they are and does not consider tokenization of the dataset. Removing strings such as \"Mar\" might lead to a bias because a bigram \"Mario Braghi\" then might become \"io Braghi\". In practical situations, it is useful to use the skip parameter to remove complicated strings such as:_`_skip = ['https://1236578995.org/@789', '@JaayZen/']_`\n* * *\n_If you enjoy my work, you can invite me_[ _for coffee_](https://www.buymeacoffee.com/petrkorab) _and support my writing. You can also subscribe to my_[ _email list_](https://medium.com/subscribe/@petrkorab) _to get notified about my new articles. Thanks!_\n## References\n[1] Koráb, & Poměnková. (2024). Arabica: A Python package for exploratory analysis of text data. Journal of Open Source Software, 9(97), 6186. \n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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