Artificial Intelligence (AI) in Sentiment Analysis and Industry Use Cases

Client Overview

Our client is a global private equity firm that invests across multiple categories, including technology, healthcare, and consumer. The client has over 300 active investments worldwide, ranging from startups to small and mid-sized businesses. The client sought to modernize their investment assessment process. Reliant on manual due diligence and a static scoring model, the client was facing bottlenecks in quickly assessing each startup and investment business.

The client sought to evaluate multiple investment opportunities simultaneously, offering rapid access to real-time business data and predictive scoring. To accomplish this, the client sought to utilize AI to revolutionize its investment decision-making process, enabling it to leverage its previous due diligence data to gain a competitive advantage.

The client engaged our team to leverage AI to create an investment scoring platform that would eliminate the data collecting process, analyze financial and market indicators and deliver a dynamic scoring for each investment.

Requirements

Develop an AI-based sentiment analysis engine that will enable the client to analyze large amounts of unstructured data from different sources, such as

• Customer and product reviews on the client’s platform
• Social media channels (Twitter, Facebook, Instagram)
• Customer service chat transcripts and survey inputs
• Sentiment analysis must go beyond polarity (positive, neutral, negative) but also understand emotion intensity & category (e.g., frustration, satisfaction, confusion).
• Develop a scalable solution that enables the integration of various multilingual data types from different parts of the world.
• Integrate with existing BI solutions for dashboards and reporting builds.
• Time-based campaigns and product launches will benefit from real-time analysis.
• Establish filters that will allow tracking and monitoring of identified keywords, competitors, product categories, or periods.

Challenges

Deploying a sentiment analysis solution at a global scale had its unique challenges:

• Data Diversity & Noise: Customers provided reviews in various formats, voices, and languages, frequently incorporating slang, typos, emojis, and sarcasm.
• Volume & Velocity: The client was receiving upwards of 2 million data points and in excess of a million reviews each month from multiple sources that needed to be processed in real-time.
• Regional Nuances: The way data is expressed in sentiment can vary widely depending on culture, geography, and language—one phrase or term can have very different sentiment outcomes from jurisdiction to jurisdiction.
• Detecting Multiple Emotions: Distinguishing layers of emotions (for instance, “frustrated but hopeful”) or statements with context-dependency was difficult to manage with traditional rule-based systems.
• Integration with Business Tools: The sentiment data needed to be available in real-time to multiple departments (like marketing, customer support, and product development) in their preferred tools (dashboards) and widely accepted business processes.

Solution Delivered By X-Byte

X-Byte delivered a high-performance AI-based sentiment analysis solution to satisfy the project requirements fully.

• Custom NLP Models: We trained custom Natural Language Processing domain models using deep learning architectures such as BERT and RoBERTa, fine-tuning the models with customer feedback by providing labeled data to optimize sentiment detection.
• Multi-language capability: Implemented language detection and translated to permit over ten languages through the API. With this capability, the analysis remains consistent across various regions.
• Emotion classification: The models classified customer sentiment into multiple classification levels beyond positive, negative, and neutral, including emotional sentiment levels such as joy, anger, fear, sadness, and surprise.
• Real-time stream processing: Apache Kafka and Spark Streaming were implemented to enable the processing of incoming data streams from social media and customer reviews in real-time.
• Visualization dashboards: Built dynamic dashboards in Power BI and Tableau with capabilities to drill down, trendlines, regional breakdowns, and sentiment heat maps.
• Automated alerts and triggers: One of the key additional features developed was that an alert mechanism that would be triggered during a negative sentiment spike or via all customer behaviors during new product launches and marketing promotional campaigns.

Industry Use Cases of AI in Sentiment Analysis

AI is disrupting industries by utilizing its new and advanced capabilities in sentiment analysis and making customer feedback much more viable as business intelligence. The following are relevant descriptions of potential key uses in the industries:

1. Retail and e-commerce

• Analyze customer devices to improve product lines and product descriptions.
• Anticipate customer satisfaction or dissatisfaction and optimize pricing or inventory.
• Change promotional campaigns based on any predisposed customer sentiment surrounding critical times of the sale and drop of products.

2. Healthcare

• Analyze patient feedback regarding a provider, treatment, or medication to improve quality measures.
• Observe how health crises, such as vaccine hesitancy or pandemics, shape public sentiment and influence the actions taken for public health and safety.
• Respond in real-time to patients’ interactions with chatbots based on their sentiment, which serves as a proxy for any stress or anxiety they may be experiencing.

3. Financial Services

• Analyze customer sentiment through the evaluation of preferences regarding their use of banking apps, customer service teams, or satisfaction with an investment product.
• Discover public sentiment surrounding financial news events to inform market analysis.
• Monitor public sentiment surrounding mentions of the brand and investor sentiment during economically distressed periods.

4. Travel & Hospitality

• Consolidate guest experience with hotel reviews and travel-centric forums.
• Change the desired product offering using hotel global competitor reviews to determine sentiment.
• Enhance customer and user experience by resolving in-situation negative sentiment in real time.

5. Media and entertainment.

• Analyze audience sentiment and reactions toward certain film releases, streaming content releases, or celebrity news events.
• Measure audience sentiment toward brands during promotional campaigns and public relations events.
• Analyze audience reactions to social media activity around the time of live events, both pre-game and post-game.

6. Politics and Government

• Analyze public sentiment during election campaign analysis as well as sentiment surrounding election debates.
• Monitor citizen sentiment toward policies and public services.
• Trace changes in misinformation while tracking sentiment related to keywords that suggest fears and anxiety.

Results

X-Byte’s sentiment analysis solution provided transformational outcomes for the customer:

• Enhanced Customer Intelligence: The client achieved granular visibility into customer sentiment at the product, category, and brand levels across all regions.
• Real-Time Intelligence: The client’s marketing teams could react to live customer feedback and pivot based on actionable insight that was in real time.
• Churn Mitigation: The client learned the sentiment triggers that led to negative feedback and was able to proactively fix customer pain points. This resulted in a churn improvement of 14%.
• Sales Conversions: The client used the sentiment data to improve product descriptions and advertisement copy, providing a 9% uplift in conversion rates on product pages.
• Strategic Decision Making: The product development organization used the sentiment trends to improve and prioritize feature enhancements based on customer expectations and underlying issues off of complaints.
• Brand Sentiment: Early notification of sentiment dissatisfaction enabled the client to respond swiftly across their social channels, thereby enhancing brand sentiment.

Conclusion

Using X-Byte’s AI-powered sentiment analysis, the client was able to tap into the full potential of its customer feedback. The automated detection and classification of sentiment across millions of data points enabled the delivery of strategic insights that improved operational performance, yielding actionable insights and measurable business growth. This project demonstrates the transformative power of artificial intelligence in converting unstructured customer data into valuable, real-time insights across various industries.