Tensorflow-Based Personality Trait Recognition Solution | ML Model

Technology Stack

Problem Statement

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Gathering data: Collecting data on customer experiences can be difficult, especially if customers are not forthcoming about their opinions.

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Interpreting data: Once data has been collected, it can be challenging to interpret and make sense of it, especially if it is complex or large in volume.

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Identifying trends: It can be difficult to identify trends and patterns in customer experiences, especially if data is disparate or not collected consistently.

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Measuring impact: It can be challenging to measure the impact of customer experiences, especially if it is not directly tied to business metrics like sales or customer retention.

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Maintaining customer trust: It can be challenging to maintain the trust of customers when collecting and using data on their experiences.

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Solution Overview

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OptiSol has created a text analytics model that collects tweets from customers about products and performs sentiment analysis to classify their emotions.

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AI models can categorize tweets into Positive, Negative, or Neutral based on the core sentiment.

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The AI model also provides more in-depth sentiment analysis such as Happiness, Sadness, Anger, Hate, and Confusion in real-time.

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The tweets and their sentiment labels are split into Train and Test sets and passed through three models: Universal Sentence Encoder (USE), LSTM, and doc2vec.

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The performance analysis showed that the USE model is the best among the options.

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Business Impact

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Improved customer satisfaction: Understanding customer sentiments can help organizations address their complaints, concerns, and needs more efficiently, leading to improved customer satisfaction.

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Increased brand reputation: By responding promptly to negative tweets and highlighting positive ones, companies can improve their brand reputation and enhance their overall image in the market.

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Better product development: Companies can use sentiment analysis to identify the most common complaints or positive feedback about their products, which can help in improving the products' quality and development.

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Improved marketing strategies: Sentiment analysis can provide valuable insights into customer preferences, opinions, and behavior, which can be used to create more effective marketing strategies and campaigns.

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Better decision making: Sentiment analysis provides a wealth of information that can be used to inform decision-making processes, from product development to customer service.

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Testimonials of Our Happy Clients

Rick Tigges

Rick Tigges

CFO, Bemodo

"They are very responsive and able to shift focus quickly as we have needed it for business reasons."
Ryan McClellan

Ryan McClellan

CEO, iLookin.com, LLC

"They [OptiSol Business Solutions] have been very friendly and they kept up with the work. They did the job as we had discussed and as everything was arranged."
Chris Gordon

Chris Gordon

CEO, Wait for me

“I can't speak enough about how pleased I am with their process; it was seamless and efficient.”
Oli Robinson

Oli Robinson

CEO, Rule:Fresh

"What impressed me the most was their willingness to go above and beyond what was asked."
Dan Talken

Dan Talken

Founder, My Equipment Library, LLC

"We're constantly impressed with their speed and quality of the development that they achieve on a consistent basis."
Ken Kisner

Ken Kisner

Molecule Corp

The best part about working with OptiSol Business Solutions is their commitment to being readily available to their partners. They’re even willing to work into late hours of the night depending on time differences. The solution they produced has already doubled efficiency levels.

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A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

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50+

AI & ML
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40+

AI & ML
Projects for
reputed Clients

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5 yrs

in AI & ML
Engineering

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