How to use AI to analyze millions of user reviews to build better products
If you release a product that receives hundreds or thousands of user reviews every day, and you have to spend a lot of time dealing with these repeated user reviews, this will stop your product from continuing to grow.
I will give a few examples of popular software products that use artificial intelligence to analyze user reviews, hopefully this will help you.
Sentiment Analysis of Amazon Reviews
Amazon is a popular online shopping store. People post reviews after purchasing an item.
If you want to outperform your competitors, you need to analyze their shortcomings.
One way to do this is to watch user reviews of competitors' products. Users will write what are the drawbacks of the product, and it's time-consuming to go through them one by one, so you can use artificial intelligence to analyze the reviews!
For example, is an Amazon review for a mobile phone charger product, and it's clear to see that
20% of users want better design.
if you cloud make a charget without designing issues, you cloud sell more products than others.
The idea I learned
It might be possible to use an AI crawler to crawl through a large number of Saas product reviews on the App Store or Product Hunt or Reddit to analyze what features of the product are most appealing to users.
Refer to
https://github.com/t-shah02/amazon-reviews-nlp-sentiment-analysis
Using Tweets to Analyze the Stock Market
Stocks are a highly volatile market and people tweet to express their views on the stock market. So it is possible to crawl a large number of tweets to analyze the change in user sentiment over a period of time.
If user sentiment is high, then there is a high probability of significant buying behavior in the stock market.
If user sentiment is low, then they are likely to sell stocks, causing the market to become more short.
Some would call this the Tweets Sentiment Index as an important indicator of the stock market.
Below is a screenshot of analytics software that makes it easy to tally what Twitter users think about a stock.
Elon Musk, in particular, causes the price of DogeCoin to rise every time he posts a tweet about it. So an AI crawler could be written to monitor when Elon Musk makes statements about cryptocurrencies and buy them quickly in seconds or something.
The idea I learned
A lot of developers tweet about the progress of their products, and you can crawl the tweets to determine how competitive the SaaS product is. For example when "Text To Speech SaaS" comes up too many times over a period of time, you know you should avoid making that product because there are too many competitors.
Refer to
Tweet Sentiment Analysis to Predict Stock Market (Standford).
Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong
github: https://github.com/AshwiniDPrabhu/Stock-Prediction-Using-Twitter-Sentiment-Analysis
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