Best Sentiment Analysis Tools

On-device sentiment analysis for .NET delivers real-time, private insights. It classifies text as positive, negative, or neutral, detects emotions like joy, anger, sadness, fear, and flags sarcasm for deeper profiling. Turn raw text into actionable intelligence for support, social listening, marketing, and product strategy.

Get insightful text analysis with machine learning that extracts, analyzes, and stores text. Train high-quality machine learning custom models without a single line of code with AutoML. Apply natural language understanding (NLU) to apps with Natural Language API. Use entity analysis to find and label fields within a document, including emails, chat, and social media, and then sentiment analysis to understand customer opinions to find actionable product and UX insights. Natural Language with speech-to-text API extracts insights from audio. Vision API adds optical character recognition (OCR) for scanned docs. Translation API understands sentiments in multiple languages. Use custom entity extraction to identify domain-specific entities within documents, many of which don’t appear in standard language models, without having to spend time or money on manual analysis. Train your own high-quality machine learning custom models to classify, extract, and detect sentiment.

Repustate provides world-class AI-powered semantic search, sentiment analysis and text analytics for organizations globally. It gives businesses the capability to decode terabytes of information and discover valuable, actionable, business insights more astutely than ever. From our esteemed clients in the Healthcare industry, to recognised leaders in Education, Banking or Governance, Repustate provides continuous deep dives into complex integrated data across industries. Our solution drives sentiment analysis and text analytics for social media listening, Voice of Customer (VOC), and video content analysis (VCA) across platforms. It encompasses the plethora of slangs, emojis and acronyms superseding the rules of formal language in social media. Whether it’s data from Youtube, IGTV, Facebook, Twitter or TikTok, or your own customer review forums, employee surveys, or EHRs, you can identify the critical aspects of your business precisely.

Semantria is a natural language processing (NLP) API from Lexalytics, leaders in enterprise sentiment analysis and text analytics since 2004. Semantria offers multi-layered sentiment analysis, categorization, entity recognition, theme analysis, intention detection and summarization in an easy-to-integrate RESTful API package. Semantria is totally customizable through graphical configuration tools, supports 24 languages, and can be deployed across private, public and hybrid clouds. Semantria scales effortlessly from single servers to entire data centers and back again to meet your on-demand processing needs. Integrate Semantria to add powerful, flexible text analytics and natural language processing capabilities to your cloud-based data analytics products or enterprise business intelligence infrastructure. Or add Lexalytics storage and visualization tools to create a complete business intelligence platform for storing, managing, analyzing and visualizing text documents.

Mine insights in unstructured text using NLP—no machine-learning expertise required—using text analytics, a collection of features from Cognitive Service for Language. Gain a deeper understanding of customer opinions with sentiment analysis. Identify key phrases and entities such as people, places, and organizations to understand common topics and trends. Classify medical terminology using domain-specific, pretrained models. Evaluate text in a wide range of languages. Identify important concepts in text, including key phrases and named entities such as people, events, and organizations. Examine what customers are saying about your brand and analyze sentiments around specific topics through opinion mining. Extract insights from unstructured clinical documents such as doctors' notes, electronic health records, and patient intake forms using text analytics for health.