Sentiment Analysis Services

iMerit delivers stellar sentiment analysis services that power Artificial Intelligence, Machine Learning, and data operation strategies. Because the most effective sentiment analysis utilize deep learning and big data to achieve the best results, having accurately labeled data is vital to getting a sentiment analysis system to work.


Analysis of datasets to identify positive, negative and neutral sentiment
Categorization of datasets, a sentiment analysis use case

What is Sentiment Analysis?

So much of interpersonal communication goes beyond the words a person uses. Sentiment analysis comes naturally to us humans, as we learn to identify and navigate non-verbal cues, tones of voice, and general demeanors that effectively convey feelings of happiness, sadness, anger, and apathy. Online, these non-verbal cues manifest as emojis, punctuation, and images such as GIFs.

Computers, by comparison, must be taught to understand the spectrum of human sentiment. Positive sentiment and negative sentiment can be subtle and need not have high polarity. Thus, training a computer to accurately detect sentiment within a given text can be a challenging task that requires high-quality human language datasets in order to be effective. Sentiment analysis is a valuable NLP application that’s built on unstructured text datasets, word classifications, positive/negative/neutral phrasing, and is over the infinite complexities of varying categories, topics, and entities within a phrase.

Sentiment analysis categorized

Sentiment analysis can best be categorized into three groups:

  1. Rule-based: these systems perform sentiment analysis automatically based on a set of manually crafted rules and a lexicon of terms with known sentiment.
  2. Automatic: these systems usually rely on machine learning techniques to learn from training data. This involves training classifiers to perform binary sentiment classification or multi-class sentiment classification when nuanced emotions (angry, amused, sad, jealous) are being considered. Open source Python toolkits such as NLTK typically implement this form of sentiment analysis.
  3. Hybrid: these systems combine both rule-based linguistics and automatic approaches to assess sentiment from a semantic perspective.

Any sentiment analysis can be deployed as an API to enable real-time access to model insights.

Importance of Sentiment Analysis Services 

Sentiment analysis empowers companies to rapidly identify online chatter about their brands and subsequently categorize it as positive, negative, or neutral. This empowers brands to better measure marketing and PR campaigns, improve customer service, and identify successful product & service features to further expand upon.

Improve Customer Experience

Customer sentiment analysis is the process of analyzing customers’ experiences and emotions in online communications like social networks or forums to find out how customers feel about products, services, or brands, and respond effectively. Categorizing and understanding customer feedback via opinion mining is key to understanding demographic trends, identifying market niches, and capitalizing on new product opportunities.

Real-Time Analysis

Real-time sentiment and text analysis can track and provide powerful insights about a brand as they appear and automatically analyze without human involvement. It can greatly enhance marketing campaigns by providing understanding of specific markets and demographics to target.

Sorting Data at scale

Once trained, sentiment analysis algorithms help businesses process the extensive amount of data in the form of chats, conversations, and other data points more affordably and efficiently.


Sentiment analysis is powered by Natural Language Processing (NLP) and Machine Learning (ML) methods and algorithms to accomplish the following:


Splitting text documents into its basic components parts like phrases, sentences, tokens, and parts of speech.


Each sentiment-related component and phrase is identified.


Sentiment scores ranging from -1 to +1 are assigned to each part of the phrase or component.


Sentiment Analysis

iMerit subject matter experts will guide you through the process to develop a customized end-to-end workflow.


Transformative, solution-based approach. Interdisciplinary sentiment analysis problem solving. Agility and responsiveness, Time-To-Value enhancers.


Targeted resources. Custom skilling. Focused and deep microlearning curriculum. Domain expertise. Rostering tools.


Alignment of sentiment analysis tools and processes. Structured Development Milestones. Two-step production and QA annotation workflows.


Transparency via analytics. Real Time Monitoring and Service Delivery Insights. Edge case Insights. Dynamic Model Improvement.


Assessment of deliverable. Appraisal of key metrics, quality control processes. Model reconsideration. Analysis of business outcome.

Sentiment Analysis Services Challenges

Sentiment analysis is a challenging workflow and there are many nuances that need to be understood while undertaking these tasks.

Sarcasm detection

People often convey negative sentiments using positive words and phrases, leading to inaccurate sentiment analysis and therefore necessitating high-quality language comprehension and contextual analysis to accurately identify and prevent.

Text Ambiguity

All text is subject to interpretation. For a text-processing application to be successful, it must be able to identify text ambiguity and effectively attach sentiment to it.

Emoticon v/s Emoji

Emojis and emoticons can be used across a breadth of circumstances to convey sentiment. The ability to understand context and emoji/emoticon application is crucial to the success of sentiment analysis.

Sentiment Analysis Case Studies

Deep dive of private equity firm reports, a sentiment analysis case study

Sentiment Analysis for a large private equity firm

Client Profile: Large private equity firm

Client data type: Images of private equity firm reports

Challenge: Building a training and validation dataset that precisely identifies and separates invaluable information from noise. As projects like this scale, so too does annotation complexity.

Volume: Over 10 million NLP data points

Outcome: The resulting pattern recognition was infused with true subject-matter expertise, and could effectively identify sentiment in the face of challenging discourse.

Sentiment analysis for speech recognition

Client Profile: Top speech recognition company

Client data type: Feedback and reviews from customers

Challenge: Annotating and identifying entities, sentiments, intents, and relationships in vast amounts of unstructured textual data like emails, reviews, customer interactions, and social media posts.

Outcome: Data was assembled to narrow the scope, context, and subject understanding to create a highly curated pipeline, with over 95% accuracy.

Feedback and review from customers, a sentiment analysis case study

Getting Started with sentiment analysis

The need for speed in high-quality sentiment analysis has never been greater. iMerit combines the best predictive and automated transcription technology with world-class data annotation and subject matter experts to deliver the data you need to get to production, fast.

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