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text sentiment analytics

What is text sentiment analysis?

Text sentiment analysis is the process of using various techniques to understand the meaning of the written text. This can include techniques such as natural language processing (NLP), sentiment analysis, and text mining. The goal of text analysis is to extract useful information and insights from large amounts of text data. This can be used for a variety of applications, such as sentiment analysis, text summarization, and topic modeling.

Text analysis tools have various applications in various industries. Due to covid and the less personal touch from commercial industries, there is a lack of personalized customer service. So text analysis provides its user an option to gather unstructured data such as text(feedback) and analyze the data then break the data into personalized services to their consumers.

text sentiment analytics

So here we will discuss the practical application of text sentiment analysis in commerce industries with practical examples and case studies to know more about its features, suitability, and functionality in commerce industries.

Commerce industries

text semantic analysis

Commerce industries refer to the sectors that are involved in the buying and selling of goods and services. Besides, these industries include a wide range of businesses and organizations, from manufacturing to wholesale to retail, to e-commerce and service providers. They play a vital role in the economy by facilitating the exchange of goods and services. They are the engine of the economy, where businesses are involved in the production and distribution of goods and services.

How is text sentiment analysis useful in commerce?

Text analytics can be used in a variety of areas in commerce, such as:

1. Retail and E-commerce

Analyzing customer reviews and feedback to improve product recommendations and customer service.

2. Marketing

Text sentiment analysis can be used to inspect customer feedback and reviews, social media posts, and other forms of customer engagement to gain insight into customer sentiment and preferences.

3. Manufacturing

Studying sensor data and other forms of machine-generated data to identify patterns and improve process efficiency.

4. Advertising

The most effective way to plan marketing and advertising campaigns is by listening to your target audience. Examine social, support, and news text data to identify trending topics, conversations, resonating content, and more. Target your audience with personalized ads and marketing campaigns.

These are just a few examples of how text analysis can be used in commerce industries. Additionally, text analysis can also be used in areas such as pricing, promotions and inventory management, market research, and customer service. Furthermore, it can also be productive in improving supply chain management, by analyzing suppliers’ performance and detecting potential. Overall, text analysis can provide a wealth of insights that can help businesses make more informed decisions and improve.

Practical application of text analysis in E-commerce and retail

In E-commerce and retail, there is a need for personal touch between buyer and seller. The text analysis tool can bridge that gap between buyer and seller and fulfilling their own needs by analyzing customer reviews and product descriptions to make personalized recommendations to shoppers. Identifying the overall sentiment (positive, negative, neutral) of customer reviews to gauge satisfaction with products and make improvements. Creating a chatbot that can understand and respond to customer queries in natural language.

text semantic analysis

All e-commerce must follow one unbreakable rule to be successful, “listen to your customers”. So anything that a business requires in terms of understanding the demand of consumers and improving their products and services text analysis tools can help. 

Text analysis can be productive in terms of the relationship between buyer and seller as follows:

1. Chatbot

Creating a chatbot nowadays is beneficial for a lot of e-commerce websites. A chatbot can understand and respond to customer queries in natural language, which is fast, accurate, and saves time for both the customer and the seller.

2. Sentiment analysis(feedback)

Retailers and e-commerce companies can use sentiment analysis to gain insights into customer satisfaction and preferences. For example, they can use this to identify common complaints or areas where customers are particularly pleased with a product or service.

3. Personalized product recommendations

Analyzing customer reviews, purchase history, and other data to generate personalized product recommendations for individual customers. Text analysis can understand consumer needs and preferences based on text and can further categorize the need of consumers which could be easy to serve for organizations.

4. Inventory management

Analyzing customer purchase history, and other data to manage inventory and ensure that popular products are always in stock.

So these are the suitability of text analysis tools in e-commerce and retail which can help them to improve their relationship with consumers. Understand the needs, and strive towards improving their quality of service and product which will reduce consumer churn for organizations.

Practical application of text analysis in marketing and advertising

Marketing and advertising is other important factor that an organization has to take care of to grow and reach the masses. So how can a text analysis tool come into place and solve the issue of marketing and advertising? 

Text analysis can be helpful in a variety of ways to help businesses in the marketing and advertising industry. It helps to gain insights from large amounts of unstructured data, such as reviews and other forms of customer engagement. 

Examples of how can text analysis tools help in marketing and advertising are:

1. social media monitoring

Businesses can use text analysis to monitor social media posts, comments, and reviews to gain insights into sentiment and preferences.

2. Brand reputation

Additionally, businesses can use text analysis to monitor and analyze customer reviews to identify potential issues and improve brand reputation.

3. Market research

By text analysis tools businesses can identify the market based on sentiment analysis. Businesses can use text analysis to gain insights into customer preferences, opinions, and demographics by analyzing customer reviews, social media posts, and other forms of customer engagement.

4. Ad targeting

Lastly, businesses can use text analysis to analyze customer reviews and other forms of customer engagement to identify key interests and preferences and use this information to target ads more effectively.

This is how text sentiment analytics can help businesses to extract insights and plan effective social marketing with personalized content. Time your marketing and advertisements accordingly to maximize success.

Real-life case study

One real-life case study of text analysis in e-commerce is by the retail giant, Amazon.


Amazon uses NLP in a variety of ways to improve the customer experience and drive sales. One example is their product recommendation system, which uses analysis tools to analyze product descriptions to make personalized recommendations. This system is based on a deep neural network trained on a large dataset of customer reviews. It can understand the meaning of the text to make more accurate recommendations.

Additionally, Amazon’s search engine uses text analysis to understand the intent behind customers’ queries and provide more relevant search results. This improves the customer experience by making it easier for shoppers to find what they’re looking for, and it also helps Amazon to boost sales.


Amazon also uses text sentiment analysis to review customers’ feedback. This allows the company to gauge customer satisfaction with its products and services and take appropriate action. This can include making improvements to products, addressing issues with customer service, or adjusting marketing strategies.

Overall, Amazon’s use of NLP in e-commerce demonstrates the potential of text analysis to improve the customer experience and drive sales. 

Final thought

We have briefly discussed how effective and efficient text analysis could be for business organizations. So which one is the best text sentiment analytics tool available in the market?


BytesView text sentiment analytics tool is one of the most effective and easiest ways to extract insights for unstructured text data. Get personalized insights to improve marketing, customer support, human resources, and more. To know more about the Bytesview product you can visit our site by clicking here.

Till next time


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