Repustate IQ Sentiment Analysis Process: Step-by-Step
Sentiment analysis is the AI-powered method through which brands can find out the emotions that customers express about them on the internet. It could be through videos on TikTok or Facebook, comments on Twitter or Xing, or surveys and emails. So, let’s dig deeper into the sentiment analysis process and understand how exactly AI enables sentiment analysis behind the curtain. We will also delve into the sentiment analysis steps conducted by Repustate IQ, the all-in-one machine text analytics and sentiment analysis solution.
Sentiment Analysis and Need of AI
Market research for functions like brand awareness campaigns, product enhancement, customer experience, brand monitoring, voice of the employee, and the like are heavily dependent on machine-learned, sentiment analysis steps of a text analytics API. Or, in other words, knowing the emotions of the audience regarding different aspects of a company - whether they are positive, negative, or neutral.
Manually sieving through big data for this kind of activity has been replaced at a breakthrough speed with AI-based technologies. Brands are choosing to lean on (natural language processing) NLP-driven machine learning platforms to get more detailed information buried in the vast sea of data that is collected through emails, customer reviews, surveys, customer care logs, voice notes, chatbots, news sources, user-generated videos on social media, social media comments, user web history, and a thousand different sources online.
Sentiment Analysis Process
Sentiment analysis steps are deeply intrinsic, comprising many different machine learning and NLP tasks and subtasks.
Step 1: Data collection
This is one of the most important steps in the sentiment analysis process. Everything from here on will be dependent on the quality of the data that has been gathered and how it has been annotated or labelled.
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API Data - Data can be uploaded through Live APIs for social media. A news API can help you glean information from all kinds of news publishers, while a Facebook API can allow you to take all the publicly available data you need from its platform. You can also use open source repositories like Kaggle, or Amazon reviews.
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Manual - If you have data that you already have from a CRM tool, you can manually upload that onto the sentiment analysis API as a .csv file.
Step 2: Data processing
The processing of the data will depend on the kind of information it has - text, image, video, or audio. Repustate IQ sentiment analysis steps also include handling video content analysis with the same ease it does text analytics. Below are the sub-tasks.
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Audio transcription - The audio from the video data is transcribed through speech to text software to ensure that any video or audio file (eg. podcast) in the data is not overlooked.
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Caption overlay - If there are any captions appearing in the video, they are extracted by Repustate IQ and analyzed for any appearing entities, aspects, or topics that you have identified as important.
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Image overlay - Similarly, the platform recognizes and captures any images in the video or text data through OCR (optical character recognition)
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Logo recognition - Repustate IQ’s intelligent data scanner immediately recognizes any logos that appear in the video background. This means even videos that appear on the clothes of the presenter or say on an item like a pen, or mug on the desk. It even picks up logos from background posters. It does it in such a way so that not even the smallest detail goes unnoticed when the platform conducts sentiment analysis of your brand.
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Text extraction- All the text is similarly recognized and extracted in the sentiment analysis process. This includes emojis and hashtags as well, which are a vital part of social media sentiment analysis. Unlike other sentiment analysis platforms, Repustate IQ ensures that emojis are never left out of data processing because that could lead to false positives or negatives.
Step 3: Data analysis
There are many subtasks that need to be done for this stage of the sentiment analysis process.
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Training the model - A set of dedicated, classified and labeled sentiment analysis of dataset that will be used to train the model needs to be pre-processed and manually labelled. It is this labelled data that will be used to train the model by comparing the correctly classified data with the incorrectly classified one. This will help improve the custom model that is created for a brand.
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Multilingual data - In sentiment analysis steps that include multilingual data processing, Repustate IQ has the dataset for each language individually annotated and trained. This is because the platform does not rely on translations at all since all the information can get distorted, or nuances lost due to vast differences in certain languages like Spanish and Korean. This is the reason why Repustate gives the highest accuracy scores compared to other platforms.
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Custom Tags - In this part of the process, custom tags for aspects and themes will be created for the data such as brand mentions, product name, etc. Once the model has been trained, it will automatically segregate text based on these custom created tags.
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Topic Classification - The topic classifier attaches a theme to a text. For example, this text “The dresses were awesome, and I found some really good scarves as well.” will be tagged as the topic “clothes”.
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Sentiment Analysis - Each aspect and theme is isolated in this stage by the platform and then analysed for the sentiment. Sentiment scores are given in the range of -1 to +1. A neutral statement may be termed as zero. This assigning of polarity is important as ultimately, even as the platform assigns the different scores to different aspects like convenience, speed, cleanliness, functionality, drinks, ambience, etc, it is the aggregate score that is calculated to know the sentiment of the audience towards the brand. So, if 3 of the 7 aspects receive a poor rating (-.65) and 4 of them receive good ones (+.5), the sentiment analyzer will give an average score as the overall sentiment of the brand.
Step 4 - Data visualization
Once all the steps in the sentiment analysis process have been covered, the insights are quickly turned into actionable reports in the form of graphs and charts. These reports can then be shared within teams as well. These visual reports are really important because it is through them that you see granular, aspect-based results. For example, when you get an average score for your brand, you can filter the results in the sentiment analysis dashboard to see which aspects got how high a score and which ones got low scores. This will give you an idea as to what areas need your attention more than others. Thus, at this stage of the sentiment analysis steps, you get actionable insights that you can use to decide the right course of action for your growth plans.
Learn more about the sentiment analysis use cases.
The Sentiment Analysis Process with Repustate IQ
Step 1 - Register & Create Project
In this step, you can register and then create a new project for the data you want to analyse.
Step 2 - Link/Upload & Process Data
In this step, you can link or upload your data onto the platform. See the videos for both options.
Step 3 - Visualise Data
In the visualization part of the sentiment analysis steps, you can see the insights from your data in the form of charts, graphs, and statistics.
Step 4 - Training your Model without Coding
In this step, you can conduct data processing by creating your own sentiment rules, and by creating custom entities per your requirements without doing any coding. See how.
Creating Custom Sentiment Rules
Learn more about applications of sentiment analysis.
Use Repustate IQ to get the most out of your investment
The aforementioned sentiment analysis process steps are integral to giving the high-precision results that Repustate IQ gives. Our all-in-one text analytics and sentiment analysis platform processes data from all sources such as news websites, patient experience data, employee experience data, customer care logs, surveys, and social channels including podcasts, YouTube, TikTok, Twitter, Facebook, and more. It is the smartest, fastest way to get accurate and precise insights from your data for market research, brand positioning, growth, you name it.
Because of Repustate IQ’s intelligent video content analysis capabilities, you can analyze videos from TikTok as easily as you can analyse text-based data like comments for business intelligence. With a named entity recognition (NER) that is arguably the best in the world, it’s no surprise that Repustate IQ beats other platforms such as even Google and Amazon in speed, language assessment, granularity, and accuracy.