Machine Overview

• Machine Name : Generic Review Sentiment Analysis
• Title : Sentiment Analysis on User Review
• Short Description :
  This machine uses a pre-trained model to analyze customer review sentiments based on extracted aspects, identifying positive, negative, or neutral feedback.
• Detail Description : This machine performs sentiment analysis on customer reviews. It extracts specific aspects from the input reviews and uses a pre-trained Hugging Face model to analyze the sentiment. The process identifies whether customer feedback is positive, negative, or neutral based on the aspects, helping to understand customer opinions and sentiments more effectively. Input Validation: The input should be a list of User Reviews and list of Aspects in the following format: ["value 1", "value 2", "value 3"]. The list should only contain string values representing review data and aspects value. If the input is not a list, or is not in the specified structure, it will not execute properly.
• Machine Document : -

Classification

• Industry : Hospitality
• Category : Sentiment Analysis
• Sub Category : -

Compatibility and Dependencies

• Dependent Machines : -
• Compatible Machines : -
• Version : 1.0.1

Specifications

• Input Specification : Download Gets lists of reviews and aspects for sentimental analysis. aspects: [String] reviews: [String]
• Output Specification : Download

  Sentiment analysis results, and review details with aspect sentiments. review_with_sentiment: dict property_name: [object] customer_reviews: [object] name: String review: String time_of_review: String user_rating: Number max_rating: Number rooms: Number service: Number location: Number domain_name: String link_of_review: String reviewsentiment: String aspectsentiment: dict room: String bathroom: String cleanliness: String staff: String service: String amenities: String breakfast: String safety: String location: String noise: String bugs: String money: String stay: String pool: String sentimentanalysis: dict aspects_values: string VERY NEGATIVE: Number NEGATIVE: Number NEUTRAL: Number POSITIVE: Number VERY POSITIVE: Number hotel_info: dict hotel_name: String rates: Number totalreview: Number rating: String hotel_address: String contact_number: String official_site_link: String ratedate: String ratingfrom: String rate_data: [object] ratefrom: String rates: Number ratedate: String

Infrastructure Requirement

• OS Requirement : Linux
• CPU : 8
• Cloud : AWS
• RAM : 16Gi

Usage Stats

• Total OnBoarded : -
• Active OnBoarding : -