Analyze customer sentiment or social media trends through our sentiment analysis services to identify positive sentiment or negative sentiment in data.contact us
Our annotators carefully assess customer reviews, enabling efficient use of NLP in customer support or any type of interaction with the user to enhance customer satisfaction and improve products or services.
By analyzing user comments and social media feedback, our annotators can detect sentiment to enable NLP systems to understand public perception of a brand. This enables proactive measures to enhance its reputation.
Our sentiment analysis experts analyze people’s moods expressed in social media posts, surveys, and online discussions, allowing our clients to gain valuable insights into consumer preferences, trends, and competitors’ performance.
We provide a secure, high-quality sentiment analysis service for your NLP algorithms that provide insights on customer sentiment, brand reputation, social media trends, and data-driven decisions.
We can perform sentiment analysis in 55 different languages. This allows for comprehensive sentiment analysis of your text data for valuable insights and well-informed decisions across diverse linguistic contexts.
Our diverse team represents various ethnicities, geographical regions, and cultural backgrounds. This way, we ensure unbiased sentiment analysis by understanding different emotional levels across nations.
For sentiment analysis, we recommend a cross-reference QA, where at least two annotators work on the same dataset. We measure their agreement rate and evaluate disagreements separately, ensuring high-quality results.
Our experts ensure that you receive the best quality annotations to develop NLP algorithms that offer valuable insights, address customer needs, and make informed business decisions.
Data collection usually happens on the client’s side. But if you don’t supply any data, our team performs data collection at your request.
At this stage, we coordinate with you the key project details to solve a particular sentiment analysis problem. Together, we decide on the process, labeling criteria, and sentiment analysis tools. We also discuss if a specific background is necessary, and if so, we proceed with hiring the right people for the project.
As we receive the first batch of text or audio data, our annotators run a small annotation sample to verify all the edge cases with you. A free pilot helps you decide whether our sentiment analysis service can meet your demands.
Once the pilot is done and the results are satisfactory, we proceed to full-scale annotation. On request, we can set up on-site teams and provide the option of working in the office. We perform sentiment analysis in batches, allowing you to track progress.
Before sending the completed annotations, we ensure their quality and validity by performing a thorough QA. For this task, we strongly advise using cross-reference QA with multiple annotators to ensure accuracy and impartiality.
Our 10+ years of experience in building remote teams allows us to expertly navigate 500+ data annotators and provide high-quality sentiment analysis services in 55 languages. Choose us as your sentiment analysis services provider for quality, speed, and security of your data.
Analyzing sentiment in multilingual social media texts.
Recruiting region-specific annotators for accurate sentiment analysis.
A US-based marketing research company faced the challenge of annotating 8000 French and Spanish texts from social media per month. To ensure accuracy and cultural understanding, annotators were sourced from the data-gathering regions (Latin America and North Africa) to capture native speakers’ slangs, detect irony, and eliminate bias.
Analyzing customer mood in audio recordings without language context.
Gathering a diverse team of annotators from various backgrounds.
A French conversational AI company tasked us to annotate 4000 hours of audio recordings featuring customer-support team conversations. The client requested at least three annotators from diverse cultures to identify customer mood solely based on voice, without contextual understanding. To ensure ethical and unbiased results, we gathered a team of 12 experts of different nationality, geography, gender, and age.
Limited access to reviews rated 2-4.
Implementing multiple rounds of data labeling for better results
For a UK-based NLP company, our team was requested to label 2000-3000 monthly reviews for restaurants and hotels on a 1 to 5 rating scale, with difficulties in accessing reviews rated 2-4. To ensure high accuracy and objectivity, these reviews underwent multiple rounds of labeling by at least two annotators, achieving exceptional precision.
Various industries, including retail, finance, social media, and customer service, rely on sentiment analysis service companies to gain valuable insights from customer feedback and enhance decision-making processes.
Sentiment analysis involves the application of both AI and ML to analyze and interpret the sentiment expressed in text data. However, sentiment analysis as a service or an AI-powered sentiment analysis system combine the capabilities of both, enabling businesses to leverage advanced technology for this task.
Similar to a sentiment analysis company, our annotators perform data collection, text preprocessing, feature extraction, multi-class sentiment classification, and result interpretation. We use NLP techniques to analyze textual data and determine the emotional tone expressed within the text.