Sentimental Analysis

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Mind Map SDLC

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INDUCCIÓN

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Sentimental Analysis

Google Cloud

Services: - AutoML - DialogFlow - Recommendations AI

Limitations: - no cross-platform functionality - make model with ease - does not require strong computers to train the model - models can be customized but require extra processing

Strengths: - sentiment is analyzed by data from customizable files - make model with ease - does not require excessive processing power since it is mostly handled by the cloud - can be used with any windows environment with multiple languages

Overview: Starting from an existing data source, an ML model is coded, trained, and then evaluated through several metrics. Next, it is deployed onto the cloud, from which data predictions, ongoing monitoring and management of the data models can continue.

Search: - Data storage can be handled with multiple methods such as BigQuery, Looker and Dataproc, providing Dataflow, cataloging and analytics.

Language: - Cloud natural language and translation is available through sentiment analysis and language detection with glossary respectively. - Language packages can also be stored, managed and modified as Artifact Registries - Natural language interpretation from Healthcare AI is possible giving real-time insights from medical data.

Speech: - Recognizes sound using text-to-speech with 220+ voices and 40+ languages

Vision: - Custom and already existing models used to detect emotion, text and other categories

Platform Comparison

Google Cloud API

Output accuracy decreases as the data size increases, but if the data is split among multiple files, accuracy reduction is mitigated somewhat.

While the data size has limits it is sufficient for most programs, specifically it allows a max size of 100GB, between 1000 to 200 million rows, between 2 to 1000 columns and at most 5 datasets.

Aside from the default labels of positive, neutral and negative, custom labels can be added by modifying the analyze method. This is done by attributing labels over certain score ranges (generally from 0 to 1)

Response parsing is done by looking for specific words/phrases and running them through the “analyze_sentiment” method.

Prebuilt models can be imported through libraries, or a custom model can also be created.

Can provide sentimental analysis to show overall attitude whether positive or negative using a numerical score.

Often predicts wrong due to large sentences as datasets might not cover variation of long sentences from which the model gets trained on

Must use big and complex datasets covering different variations. But no limit of characters that can be used to run the prediction

Not enough pre built models forcing users to make and train models

Can use word logger to identify or search for specific words and its corresponding synonyms within sentences or paragraphs

The user need to create their own model to perform certain predictions providing easability, features and flexibility

Also able to deliver and perform sentiment analysis on structured and unstructured text

Have data limits where the document(sentences) must be under 5,120 characters long per document.

Limited classification of labels (only positive, negative or neutral) , allowing no addition of custom sentiment labels by the developer

Opinion mining feature provides a service, where it picks up on opinions as target (noun or verb) and an assessment (adjective) for these targets, essentially providing more granular information about opinions related to attributes of products or services in text.

Does not need to train model to do predictions and provide confidence score

Fully capable of delivering and performing sentimental analysis on structured or unstructured text.

Core ML

Text Classifier

Services: - word search - sentiment prediction

Limitations: - can only be used in apple devices limited to iOS and macOS - need bigger with variation dataset to make the model effective - not enough pre built models

Strengths: - can predict the sentiment from a text - make model with ease - does not require strong computers to train the model - convert any model into readable core ml model format - can be used in iOS or macOS applications

Overview: Core ML is an Apple framework in where developers can easily create machine learning models can integrate that model or existing model that Core ML already offers into their iOS or MacOS applications.

Search: Search for any relevant information from a content. E.g Resnet50, MobileNetV2, SqueezeNet

Language: Identifies sentiments or words within natural language. E.g Text Classifier, Word tagger

Speech: Identifies sound within video or audio clips. E.g Sound Classifier

Vision: Identifies objects within images or videos. E.g Image classification, object detection and Action Classification

Azure Cognitive Services

Text Analytics API

Tutorial Link

Services: - Opinion Mining - Language Detection - Named Entity Recognition - Key Phrase Extraction - Personally Identified Information Recognition

Limitations: - Limitation of under 5,120 characters long per document - Limited sentimental labels (no custom labels can be added) - Quality decreases for longer texts - Not suitable for long feedback or reviews

Strengths: - Applicable for structured or unstructured text - Provides sentiment labels (positive, negative, neutral) - Provide confidence scores ranging from 0 to 1 to indicate the confidence and probability in the label’s classification

Overview: Provides natural language processing from raw text for sentimental analysis as well as key phrase extraction and language detection.

Platform Services

Search: Identifies and extracts content relevant from user’s query. E.g Bing Video Search, Bing Visual Search, Bing Local Business Search

Language: Identifies and extracts meaning from unstructured text through intelligent analysis. E.g Text Analytics, Immersive Reader, QnA Maker

Speech: Identifies and integrates speech processing functionality to app and services. E.g Speech to Text, Text to Speech, Speech Recognition

Vision: Identifies and analyses content for example images and videos. E.g Computer vision, Form Recognizer, Face Recognizer

Decision: Accelerates decision making and allows smart decision making. E.g Anomaly Detector, Content Moderator