Kimola Cognitive VS NLP Cloud

Kimola Cognitive is a rock-solid Machine Learning Platform that enables users to grab reviews from 20+ channels and analyze + classify customer feedback -or any text data- automatically. Here are TOP skills of Kimola Cognitive:

• Scrape Web and Collect Reviews Data analysis starts with data collection, and we made it as easy as pie. Kimola offers a web browser extension for marketing and research professionals to scrape content from the web to analyze and classify. It supports over 20 mediums, such as Amazon, Yelp, Youtube, Capterra, and TrustPilot. Add this extension to your browser and set it up with one easy step to get started.

• Pre-Built models There is a gallery of ready-to-use machine learning models for the most common use cases like sentiment and hate speech analysis along with consumer conversations around SaaS products, mobile apps, games. Choose one or more models to classify your data.

• Create & store custom machine learning models Every research is a different journey to understand humankind, so requirements will usually be unique. That's why Kimola Cognitive also supports creating custom Machine Learning models by training your own dataset. The platform takes care of choosing the best performing statistical model for your training set to ensure the best accuracy rate possible.Your custom machine learning models are hosted on Kimola Cognitive and can be used via the user interface and API.

NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, question answering, machine translation, language detection, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API.

You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models.