{"id":163405,"date":"2022-10-25T11:01:37","date_gmt":"2022-10-25T15:01:37","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=163405"},"modified":"2022-11-21T09:59:21","modified_gmt":"2022-11-21T14:59:21","slug":"how-to-get-tweets-using-python-and-twitter-api-v2-part-iii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/how-to-get-tweets-using-python-and-twitter-api-v2-part-iii\/","title":{"rendered":"How to get Tweets using Python and Twitter API v2 \u2013 Part III"},"content":{"rendered":"\n<p><em>See <a href=\"\/campus\/ibkr-quant-news\/how-to-get-tweets-using-python-and-twitter-api-v2-part-i\/\">Part I<\/a> for an overview of the Tweepy client for Twitter API v2, and <a href=\"\/campus\/ibkr-quant-news\/how-to-get-tweets-using-python-and-twitter-api-v2-part-ii\/\">Part II<\/a> to learn how to get Tweet(s) with Tweet Id(s) using client.<\/em><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"pagination-in-client\">Pagination in client<\/h3>\n\n\n\n<p>We have mentioned earlier that the max_results parameter while searching tweets can have a maximum value of 100. So what do we do if we need more than 100 tweets? The answer is pagination.<\/p>\n\n\n\n<p>Pagination in Client is similar to how we used Cursors for the API. Let us see an example of how we can fetch 1000 tweets. We have taken the same query that we used in the previous section.<\/p>\n\n\n\n<p>Want another example? Here you go.<\/p>\n\n\n\n<p>We have covered some of the important methods to fetch data from Twitter. There are many more to explore. If you would like to read about all the methods that are available, please read the official Client documentation&nbsp;<a href=\"https:\/\/docs.tweepy.org\/en\/stable\/client.html\" target=\"_blank\" rel=\"noreferrer noopener\">\u207d\u2077\u207e<\/a>.<\/p>\n\n\n\n<p>The methods for the Client return a Response object with the results. In the subsequent sections, we will look at how we can save the search results to different data formats for analysis.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"using-expansions-to-get-user-and-media-information\">Using expansions to get user and media information<\/h3>\n\n\n\n<p>The Twitter API provides expansions&nbsp;<a href=\"https:\/\/developer.twitter.com\/en\/docs\/twitter-api\/expansions\" target=\"_blank\" rel=\"noreferrer noopener\">\u207d\u2078\u207e<\/a>&nbsp;as a means to expand the payload we get from a search for a user or tweet lookup. Expansions help include additional data in the same response without the need for separate queries.<\/p>\n\n\n\n<p>We can expand on the&nbsp;<em><strong>attachments.media_keys<\/strong><\/em>&nbsp;to view the media object,&nbsp;<em><strong>author_id<\/strong><\/em>&nbsp;to view the user object, and&nbsp;<em><strong>referenced_tweets.id<\/strong><\/em>&nbsp;to view the Tweet object the originally requested Tweet was referencing. The expanded objects are nested in the&nbsp;<em><strong>includes<\/strong><\/em>&nbsp;object.<\/p>\n\n\n\n<p>Let us look at an example to get the user and media information using expansion:<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"writing-the-search-results-to-a-text-file\">Writing the search results to a text file<\/h3>\n\n\n\n<p>Let us now save the search results of a query to a text file.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"putting-the-search-results-into-a-dataframe\">Putting the search results into a DataFrame<\/h3>\n\n\n\n<p>Pandas DataFrames are great for working with a large amount of data. So let us save our search results as a Pandas DataFrame. If you would like to save this data in a csv file, you can use the Pandas to_csv() method.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"twitter-api-v2-github\">Twitter API v2 GitHub<\/h3>\n\n\n\n<p>We have attempted to cover most of the common use cases for pulling Twitter data. If you want to explore the Twitter API v2 further, this Twitter Developer Platform Resources&nbsp;<a href=\"https:\/\/github.com\/twitterdev\" target=\"_blank\" rel=\"noreferrer noopener\">\u207d\u2079\u207e<\/a>&nbsp;repository is a great place to start. This repository has many sample codes&nbsp;<a href=\"https:\/\/github.com\/twitterdev\/Twitter-API-v2-sample-code\" target=\"_blank\" rel=\"noreferrer noopener\">\u207d\u00b9\u2070\u207e<\/a>&nbsp;for the versatile functionality offered by the Twitter API v2.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"conclusion\">Conclusion<\/h3>\n\n\n\n<p>In this two-part blog series, we explored how we can pull data from Twitter using the API and Client interfaces of the Tweepy library. We also looked at Twitter APIs and their functionalities.<\/p>\n\n\n\n<p>Now that we have nicely organised data in Pandas DataFrames, we can perform natural language processing on it. Want to learn more? Don&#8217;t forget about our great course on&nbsp;<a href=\"https:\/\/quantra.quantinsti.com\/course\/trading-twitter-sentiment-analysis\" target=\"_blank\" rel=\"noreferrer noopener\">Twitter Sentiment Analysis<\/a>, check it out and go live trading!<\/p>\n\n\n\n<p>Till then, happy coding!<\/p>\n\n\n\n<p><em>Visit QuantInsti for additional insights on this topic:&nbsp;<a href=\"https:\/\/blog.quantinsti.com\/twitter-api-v2\/\">https:\/\/blog.quantinsti.com\/twitter-api-v2\/<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We have mentioned earlier that the max_results parameter while searching tweets can have a maximum value of 100. So what do we do if we need more than 100 tweets? The answer is pagination.<\/p>\n","protected":false},"author":731,"featured_media":151496,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,350,341],"tags":[806,4582,865,2859,1224,595,1038,12358],"contributors-categories":[13654],"class_list":{"0":"post-163405","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-python-development","10":"category-ibkr-quant-news","11":"category-quant-asia-pacific","12":"category-quant-development","13":"tag-data-science","14":"tag-dataframe","15":"tag-github","16":"tag-natural-language-processing","17":"tag-pandas","18":"tag-python","19":"tag-sentiment-analysis","20":"tag-twitter-api","21":"contributors-categories-quantinsti"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>How to get Tweets using Python and Twitter API v2 \u2013 Part III<\/title>\n<meta name=\"description\" content=\"We have mentioned earlier that the max_results parameter while searching tweets can have a maximum value of 100. 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