The rise of artificial intelligence has formed a trail of disruptive technologies. From computer vision and natural language processing to predictive analytics and recommendation engines, AI is rapidly transforming global business services. Sentiment analysis is one such AI-driven technology that channelizes extensive digital information to trace the underlying emotions of textual data. An AI development company executes dynamic applications of sentiment analysis to automate and empower the decision-making capabilities of businesses worldwide.
This blog post explores some potential business applications of sentiment analysis across industries.
The Science Behind Sentiment Analysis
Natural language processing (NLP) or is the underlying technology in sentiment analysis that combines AI, computer linguistics, and computer science. NLP traces the positive and negative undercurrents of documents, social media posts, and other unstructured interactions using the following techniques-
1) Sentence Segmentation
2) Word Tokenization
3) Part-of-speech Classification
4) Text Lemmatization
5) Stop word Identification
6) Dependency Parsing
7) Noun Classification
8) Named Entity Recognition
9) Coreference Resolution
NLP at Work. Source- Slideplayer
The above-mentioned steps are broadly categorized as the syntactic and semantic analysis under NLP. It enables machines to analyze the content’s grammatical sense followed by algorithmic applications to trace the emotion, behavior, and context.
Let’s explore some effective applications of sentiment analysis.
Sentiment Analysis for Banking and Finance Industry
With rapid digitization, banking operations have experienced a revolutionary shift in structure and management. Automation in financial institutions is gaining pace with applications of emerging technologies such as IoT, artificial intelligence, and machine learning.
From on-boarding new customers to accounting to cybersecurity, global banking players are proactively exploring AI-driven automation to scale processes. AI-powered sentiment analysis is one of the potential business applications for banks to channelize complex transactional datasets. With a view to streamlining market analysis and customer services, banks can apply sentiment analysis in the following fields-
a) Analyzing customer expenditure pattern
track personal expenditure patterns
b) Strengthening Investment Strategies
With an explosion of digital information, social media interactions, and news highlights, it becomes cumbersome for banks to make informed decisions. NLP techniques overcome this limitation by classifying relevant news, articles, and comments to provide a real-time sentiment score. It enables banks to formulate well-informed investment strategies by extracting the risk signals out of market interactions.
Customer Sentiment Analysis for eCommerce Businesses
The eCommerce industry has witnessed a major priority shift from focusing on mere transactions to improving customer services and experiences. To accelerate the improvement of business operations, eCommerce giants are already experimenting with multifaceted AI technologies across vectors.
The algorithmic advancements in AI’s cognitive abilities are replacing human intervention in maintaining customer relations and loyalty.
Mckinsey’s lab research reveals that effective integration of AI-powered chatbots on eCommerce portals impacts customer purchases positively.
AI can feed on chains of customer opinions and reviews about different products to provide accurate sentiment analysis. It enables eCommerce businesses to extract workable insights about the overall demands, needs, interests, and future preferences of their target audience.
Here’s how businesses can deploy sentiment analysis to deliver optimum customer support across channels-
a) Social media discussion rooms and customer interactions become a major source of data for NLP technologies to analyze customer emotions.
b) NLP technologies for tracking customer feedback assists machine learning models to make accurate product recommendations to the target audience.
c) Website chatbots powered by NLP technologies can examine diverse customer queries to analyze pain points and thereby improve customer support.
Optimizing Applications of Sentiment Analysis with Oodles AI
We, at Oodles AI, have experiential knowledge in training machine learning models with historical customer data to derive-
a) Sentiment analysis
b) Threat detection
c) Visual question answering
d) Intelligent document validation, and more.
Our models are effective at scanning unstructured social media posts, comments, and interactions to analyze market and customer behavior. The combination of our NLP and chatbot development services can extract meaningful insights that improve multiple business operations effectively.
