These days, everyone and their furry mates (no jokes) have Instagram accounts. Truthfully, Instagram captions (the text that a user writes while posting a photo) are a great source to get textual data that can be mined and analyzed easily.
Even though the importance of emotions in marketing is well known, still quantifying emotions is not as easy as measuring data metrics – likes, mentions, and comments. But, with sentiment analysis today, brands can translate those feelings into actionable business data with ease.
Simply put, Instagram sentiment analysis is an ideal choice to discover and attract new target audiences and also improve brand presence. Data shows that the projected marketing spending on Instagram influencer-led marketing programs is predicted at $8.08 billion.
Sentiment analysis improves your operational efficiency as it gives you a peek into the reason behind the good and bad sentiments in posts and comments. That’s why using an Instagram sentiment analysis, can help you gauge aspects like brand awareness, and brand image and predict consumer behavior.
Here in this blog, we will shed light on how to implement sentiment analysis on Instagram, and how it can benefit you.
Which Formats are Used for Extracting Customer Insights From Instagram Social Listening?
The primary formats for getting sentiment analysis on Instagram includes posts, comments, hashtags, videos, and Instagram TV. Let’s find out.
Post and Comments
You can identify the JSON data from a specific URL account to discover insights like followers, account name, people followed, number of posts etc. Even, a web scraper tool is used for more in-depth data collection.
Instagram’s official API offers access to Instagram data that is related to your account. So, to get customer insights from other accounts, you’ll need to work outside of Instagram’s API along with a web scraper. A web scraper automatically extracts data from the platform by sending HTTP requests to different web pages focusing on downloading them. Then, the data is parsed and saved to a database where the visualization software is used to make sense of the information.
One approach of scraping Instagram posts and comments is through Python. A Python-based insight-gathering tool like Selenium is a hot pick of marketers.
Hashtags
Hashtags are an excellent choice for extracting sentiment analysis on Instagram. With an Instagram Hashtag scraper like Instascrape, you can quickly vet 22 different data points surrounding a single Instagram hashtag to compile data related to those hashtags.
This includes commonly associated words and accounts. Apify is an excellent Instagram scraper used to customize focus on hashtags added to profiles, comments, posts, and places. This includes several different automation tools in addition to a scraper. The data output format is in JSON.
Videos and IGTV
Instagram TV (IGTV) and reel are both equally beneficial. These scrapers provide different data types, like recent comments, full JSON data, etc.
Instascrape is a popular video scraper for Instagram. This can scrape long-form videos on IGTV, story videos, post videos, reels, and more. The syntax remains simple and offers flexible Instagram data in general.
What Are The Benefits of Using Sentiment Analysis On Instagram
Sentiment analysis on Instagram helps you keep track of brand positioning with customer insights. Also, it helps you avert a PR crisis and strengthen your branding efforts.
Without any ado, let’s look at some of the major benefits:
Stronger Brand Performance
You would have thousands of Instagram posts that collect engagements but none to understand them. Instagram sentiment analysis provides all the required avenues needed to understand how your business account is doing.
Better Audience
Instagram sentiment analysis helps you understand the audience better. You can interpret user engagement, demographic behavior, and brand mentions, and find the nuances in emotions.
Competitor Analysis
With Instagram sentiment analysis you can track your competitors better. Watching how your audience engages with your competitors will provide you with perspectives that you wouldn’t have got otherwise.
Focused Marketing
Undoubtedly, marketing and advertising campaigns benefit significantly from sentiment analysis of Insta comments and videos. Also, it helps you identify potential mistakes in marketing strategies.
Groundbreaking Customer Service
Instagram sentiment analysis helps you improve your customer service program and bridge the gaps if any.
Improvised Product
Sentiment analysis of Instagram gives insight into product enhancement including new features, optimal pricing, and many other aspects.
Finally, How To Use Instagram Sentiment Analysis Solution?
Data collected from Instagram videos are edited and processed for text analytics to disambiguate entities. Further, they are processed for sentiment analysis presenting data on a dashboard.
Below, are the steps describing how a sentiment analysis solution extracts data from this platform:
#Step 1: Speech to Text Transcription
All video files collected for sentiment analysis on Instagram are converted into text using a speech-to-text model. Besides, data can be gleaned using web scraping tools. Several open-source scrapers do this job. Some are:
- Instalooter
- Instagram Scraper
- Instaloader
- Instagram PHP Scraper
- Socialmanagertools Igbot
#Step 2: Caption Overlay
Videos are broken frame-by-frame into image formats. Any text that appears in these frames is identified and extracted. Once the scraper finds the caption, it can scrape the last ten or so captions from the respective page in one instance.
The Instagram Graph API and Instagram Basic Display API are considerable choices for this purpose.
#Step 3: Image Recognition
Similar to captions, with video analysis AI, the model also recognizes logos and images present in the background. Brand names and logos are identified and classified as entities.
Read further at: Benefits Of Using Sentiment Analysis on Instagram
Artificial intelligence (AI) and machine learning are becoming more integrated in many aspects of social media. AI is far from replacing human touch in the field of social media, but it is increasing both the quantity and quality of online interactions between businesses and their customers.
This article discusses 4 machine learning tools that can enhance social media marketing: social media monitoring, sentiment analysis, image recognition, and chatbots. The article includes real-life examples of how major businesses such as UNIQLO, The Container Store, GameStop, Airbnb, and Estee Lauder are actually using these machine learning techinques on their Facebook, Twitter, and Instagram accounts to build relationships with their customers through social media.
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