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Sentiment Analysis using AI with Natural Language Processing (NLP) 

Understanding customer sentiment about products and services is crucial for businesses to grow and succeed. To accomplish this goal, businesses focus effort on collecting customer feedback through social media monitoring or Net Promoter Score (NPS) surveys in the forms of marketplace product reviews and feedback campaigns. However, sorting and analyzing this data to categorize results is labor intensive and time consuming. What if the process could be automated?

AI with NLP provides a powerful way for text data analysis to retrieve valuable insights about customer sentiment. AI engines sort through the text, homing in on the words which convey opinion or emotion. At the most general level, the results are then analyzed to determine if the sentiment falls into a negative, neutral or positive category. However, more complex algorithms break down those categories into finer degrees of sentiment (very positive/somewhat positive/etc.) or feelings regarding different aspects of a product (usability, price, performance).

There are different ways to approach the analysis of data using AI with NLP, however they all start with cleaning and normalizing the data.

Starting with Clean Data

Before any data analyzation can begin, the data must be standardized and normalized. Below is a high-level look at some of the steps which may be used in getting data ready for analysis.

Identifying and Removing Unwanted Characters

Unwanted characters include data such as punctuation, tags, and symbols. This is also referred to as noise, as this may interfere with proper analyzation.

Standardizing Text

Standardized text includes fixing spelling errors, removing unwanted spaces or lines, and converting all text to the same format, such as lower case.

Breaking down sentences and removing unnecessary words

This aspect of cleaning data includes Tokenization, Stop word removal and Lemmatization. Tokenization is the process of breaking down text into units of words or tokens which are meaningful to the text analysis. Stop words such as is, the, and are removed as they are not useful in conveying sentiment. Lemmatization is converting words to their base form (walked or walks to walk) to further standardize the data.

There are several tools you can use to implement the steps above. For instance, if using Python – the Natural Language Toolkit (NLTK) is a popular open-source library for NLP. Other libraries include spaCy, TextBlob and regex.

Getting Results

The goal of sentiment analysis is to better understand how customers feel about your products and services. The traditional methods of compiling these results, especially when large datasets were being considered, often took many weeks of analysis and reporting – delaying any beneficial adjustments which could be made.

Using AI and NLP to perform sentiment analysis, particularly on large datasets, significantly reduces the amount of required processing time. Most of the time spent on this process will be building algorithms and training the models, which can be accomplished up front. Once the process has been trained, then data can be input and processed, attaining much faster results.

Proactive decision making

By adopting AI based sentiment analysis into business processes, companies compile valuable information of customer opinions and preferences. Tools like Python and NLP enable businesses to monitor sentiment trends over time, identify possible issues, and take necessary actions to overcome them.


AI and NLP empower businesses to extract actionable insights from text data and make informed decisions. By leveraging sentiment analysis, companies can enhance customer satisfaction, improve brand reputation, and stay ahead of the competition. With the use of NLP libraries available in Python, businesses can develop AI based tools which will help businesses to succeed in the current competitive world.

About SphereGen

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SphereGen is a unique solutions provider that specializes in cloud-based applications, Intelligent Automation, and Extended Reality (AR/VR/MR). We offer full-stack custom application development to help customers employ innovative technology to solve business problems.

Learn more about what we do in Intelligent Automation: https://www.spheregen.com/intelligent-automation

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