Real-time sentiment analysis using AI technology

Problem Statement

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

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.

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

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.

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

Solution Overview

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

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

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

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.

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

Business impact

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

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.

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.

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.

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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