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How To Use Real-Time Sentiment Analysis For Live Social Feeds

Real-time sentiment analysis is an important artificial intelligence-driven process that is used by organizations for live market research for brand experience and customer experience analysis purposes. In this article, we explore what is real-time sentiment analysis and what features make for a really brilliant live social feed analysis tool.

What Is Real-Time Sentiment Analysis?

Real-time Sentiment Analysis is a machine learning (ML) technique that automatically recognizes and extracts the sentiment in a text whenever it occurs. It is most commonly used to analyze brand and product mentions in live social comments and posts. An important thing to note is that real-time sentiment analysis can be done only from social media platforms that share live feeds like Twitter does.

The real-time sentiment analysis process uses several ML tasks such as natural language processing, text analysis, semantic clustering, etc to identify opinions expressed about brand experiences in live feeds and extract business intelligence from them.

Why Do We Need Real-Time Sentiment Analysis?

Real-time sentiment analysis has several applications for brand and customer analysis. These include the following.

  1. Live social feeds from video platforms like Instagram or Facebook
  2. Real-time sentiment analysis of text feeds from platforms such as Twitter. This is immensely helpful in prompt addressing of negative or wrongful social mentions as well as threat detection in cyberbullying.
  3. Live monitoring of Influencer live streams.
  4. Live video streams of interviews, news broadcasts, seminars, panel discussions, speaker events, and lectures.
  5. Live audio streams such as in virtual meetings on Zoom or Skype, or at product support call centers for customer feedback analysis.
  6. Live monitoring of product review platforms for brand mentions.
  7. Up-to-date scanning of news websites for relevant news through keywords and hashtags along with the sentiment in the news.

Read in detail about the applications of real-time sentiment analysis.

How Is Real-Time Sentiment Analysis Done?

Live sentiment analysis is done through machine learning algorithms that are trained to recognize and analyze all data types from multiple data sources, across different languages, for sentiment.

A real-time sentiment analysis platform needs to be first trained on a data set based on your industry and needs. Once this is done, the platform performs live sentiment analysis of real-time feeds effortlessly.

Below are the steps involved in the process.

Step 1 - Data collection

To extract sentiment from live feeds from social media or other online sources, we first need to add live APIs of those specific platforms, such as Instagram or Facebook. In case of a platform or online scenario that does not have a live API, such as can be the case of Skype or Zoom, repeat, time-bound data pull requests are carried out. This gives the solution the ability to constantly track relevant data based on your set criteria.

Step 2 - Data processing

All the data from the various platforms thus gathered is now analyzed. All text data in comments are cleaned up and processed for the next stage. All non-text data from live video or audio feeds is transcribed and also added to the text pipeline. In this case, the platform extracts semantic insights by first converting the audio, and the audio in the video data, to text through speech-to-text software.

This transcript has timestamps for each word and is indexed section by section based on pauses or changes in the speaker. A granular analysis of the audio content like this gives the solution enough context to correctly identify entities, themes, and topics based on your requirements. This time-bound mapping of the text also helps with semantic search.

Even though this may seem like a long drawn-out process, the algorithms complete this in seconds.

Step 3 - Data analysis

All the data is now analyzed using native natural language processing (NLP), semantic clustering, and aspect-based sentiment analysis. The platform derives sentiment from aspects and themes it discovers from the live feed, giving you the sentiment score for each of them.

It can also give you an overall sentiment score in percentile form and tell you sentiment based on language and data sources, thus giving you a break-up of audience opinions based on various demographics.

Step 4 - Data visualization

All the intelligence derived from the real-time sentiment analysis in step 3 is now showcased on a reporting dashboard in the form of statistics, graphs, and other visual elements. It is from this sentiment analysis dashboard that you can set alerts for brand mentions and keywords in live feeds as well.

Learn more about the steps in sentiment analysis.

What Are The Most Important Features Of A Real-Time Sentiment Analysis Platform?

A live feed sentiment analysis solution must have certain features that are necessary to extract and determine real-time insights. These are:

  • Multiplatform

One of the most important features of a real-time sentiment analysis tool is its ability to analyze multiple social media platforms. This multiplatform capability means that the tool is robust enough to handle API calls from different platforms, which have different rules and configurations so that you get accurate insights from live data.

This gives you the flexibility to choose whether you want to have a combination of platforms for live feed analysis such as from a Ted talk, live seminar, and Twitter, or just a single platform, say, live Youtube video analysis.

  • Multimedia

Being multi-platform also means that the solution needs to have the capability to process multiple data types such as audio, video, and text. In this way, it allows you to discover brand and customer sentiment through live TikTok social listening, real-time Instagram social listening, or live Twitter feed analysis, effortlessly, regardless of the data format.

  • Multilingual

Another important feature is a multilingual capability. For this, the platform needs to have part-of-speech taggers for each language that it is analyzing. Machine translations can lead to a loss of meanings and nuances when translating non-Germanic languages such as Korean, Chinese, or Arabic into English. This can lead to inaccurate insights from live conversations.

  • Web scraping

While metrics from a social media platform can tell you numerical data like the number of followers, posts, likes, dislikes, etc, a real-time sentiment analysis platform can perform data scraping for more qualitative insights. The tool’s in-built web scraper automatically extracts data from the social media platform you want to extract sentiment from. It does so by sending HTTP requests to the different web pages it needs to target for the desired information, downloads them, and then prepares them for analysis.

It parses the saved data and applies various ML tasks such as NLP, semantic classification, and sentiment analysis. And in this way gives you customer insights beyond the numerical metrics that you are looking for.

  • Alerts

The sentiment analysis tool for live feeds must have the capability to track and simplify complex data sets as it conducts repeat scans for brand mentions, keywords, and hashtags. These repeat scans, ultimately, give you live updates based on comments, posts, and audio content on various channels. Through this feature, you can set alerts for particular keywords or when there is a spike in your mentions. You can get these notifications on your mobile device or via email.

  • Reporting

Another major feature of a real-time sentiment analysis platform is the reporting dashboard. The insights visualization dashboard is needed to give you the insights that you require in a manner that is easily understandable. Color-coded pie charts, bar graphs, word clouds, and other formats make it easy for you to assess sentiment in topics, aspects, and the overall brand, while also giving you metrics in percentile form.

The user-friendly customer experience analysis solution, Repustate IQ, has a very comprehensive reporting dashboard that gives numerous insights based on various aspects, topics, and sentiment combinations. In addition, it is also available as an API that can be easily integrated with a dashboard such as Power BI or Tableau that you are already using. This gives you the ability to leverage a high-precision sentiment analysis API without having to invest in yet another end-to-end solution that has a fixed reporting dashboard.


Repustate IQ is completely customizable to your business needs. This leads to greater accuracy and relevancy of outputs because your market research can be based on your industry-speak and entities such as product names, competitors, customer demographics, etc that are relevant to you.

Furthermore, the solution allows repeat scans every 24 hours on all platforms for hashtags or keywords and generate alerts based on different triggers. This frequency can be increased when necessary based on your needs. Once trained, the model keeps getting smarter with time as it processes more and more data, thus giving you a return on investment that keeps on increasing.

Repustate’s premium real-time sentiment analysis solution is available as an on-prem installation as well as on the cloud.