Twitter Data Analytics

Where to get Twitter data for academic research
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Contents:

  • Top 13 Twitter Analytics Tools in 12222
  • Text Processing and Sentiment Analysis of Twitter Data
  • How to Use Twitter Analytics
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  • Getting Started

    • Draw valid data-backed conclusions from your Twitter marketing performance. Gain helpful competitive advantage with social media benchmarking to optimize your social marketing performance.

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    Easily browse through all of them in our handy metric library. Customize and arrange metrics on flexible dashboards that exactly fit your specific social media goals. Discover how your audience engages with your content, how many retweets you receive and when you get mentioned. Understand your Twitter marketing performance better by analyzing private-level metrics. Find out how many impressions your tweets receive and how your followers engage with it.

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    Know what users mention besides your Twitter account and learn more on what they also talk about. With this information you can further optimize your strategy based on solid data. Easily convert your analysis into automated recurring reports. This saves a lot of time and effort. Share your dashboards as dynamic web pages for your colleagues or clients to explore. Get in touch today to track, benchmark and optimize your social media performance with advanced analytics, simplified.

    Top 13 Twitter Analytics Tools in 12222

    Use cases. Brands Find the KPIs you need, get deep insights and build your brand by understanding your audience on social. Agencies Drive better decisions based on quality data using social analytics and reporting for all your clients. Case Study: The Marketing Arm How this global agency uses social intelligence to win pitches and retain clients with top brands.

    Media Do more with your data using custom metrics and API integrations to optimize your social strategy.

    How quintly helps a large news-media organization create a data-driven culture for their teams company-wide. Analytics Track, benchmark and optimize your social media. Data in Depth Secure, trusted data quality for better business. Reporting Create or automate reports, exports and live links.

    Text Processing and Sentiment Analysis of Twitter Data

    Facebook Instagram Twitter Snapchat New! LinkedIn YouTube Pinterest. Careers Looking for a job? See what we have to offer.

    Contact Want to get in touch? Enroll now! Learn more. In this lesson you will explore analyzing social media data accessed from twitter, in R.

    How to Use Twitter Analytics

    Analytics. Measure and boost your impact on Twitter. Sign in with Twitter. Meet your dashboards. Tweet activity. Measure engagement and learn how to make. Seven things you can learn from your Twitter data. Twitter analytics provides a wealth of information that can help you create meaningful Tweets that will.

    To setup your app, follow the documentation from rtweet here:. Once you have your twitter app setup, you are ready to dive into accessing tweets in R. The first thing that you need to setup in your code is your authentication. When you set up your app, it provides you with 3 unique identification elements:.

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    These keys are located in your twitter app settings in the Keys and Access Tokens tab. You will need to copy those into your code as i did below replacing the filler text that I used in this lesson for the text that twitter gives you in your app. Finally, you can create a token that authenticates access to tweets!

    Note that the authentication process below will open a window in your browser. If authentication is successful works, it should render the following message in a browser window:. Authentication complete. Please close this page and return to R.

    Getting Started

    Now you are ready to search twitter for recent tweets! To see what other arguments you can use with this function, use the R help:. It is similar to sharing in Facebook where you can add a quote or text above the retweet if you want or just share the post. This function returns just a data. First, where are they from? Note that in this case you are grouping your data by user. It looks like you have some NA or no data values in your list. Looking at your data, what do you notice that might improve this plot? There are unique locations in your list.