AI Powered Sentiment Analysis AI CD

Slide 1: This slide introduces AI-powered Sentiment Analysis. State Your Company Name and begin.
Slide 2: This slide is an Agenda slide. State your agendas here.
Slide 3: This slide shows a Table of Contents for the presentation.
Slide 4: This slide is an introductory slide.
Slide 5: This slide provides information regarding sentiment analysis which helps in detecting the emotional tone of user’s message.
Slide 6: This slide entails information regarding role of sentiment scoring which helps in detecting how consumers feel about products offered.
Slide 7: This slide presents information regarding criticality of sentiment analysis approach which helps in identifying objective insights.
Slide 8: This slide puts information regarding pros and cons associated with sentiment analysis technique.
Slide 9: This slide mentions information regarding various issues associated with sentiment assessment.
Slide 10: This slide highlights information regarding essential best practices that help in the ethical usage of sentiment analysis by considering focus areas.
Slide 11: This slide is in continuation with the previous slide.
Slide 12: This slide is an introductory slide.
Slide 13: This slide illustrates information regarding various through sentiment analysis can be performed in terms of sentiment detection.
Slide 14: This slide depicts information regarding several types of sentiment analysis which helps in extracting human sentiments to develop better insights.
Slide 15: This slide is in continuation with the previous slide.
Slide 16: This slide demonstrates information regarding several approaches deployed for sentiment classification in terms of machine learning-based.
Slide 17: This slide mentions information regarding comparison of sentiment and semantic analysis based on several parameters.
Slide 18: This slide puts information regarding use cases of sentiment analysis.
Slide 19: This slide is in continuation with the previous slide.
Slide 20: This slide is an introductory slide.
Slide 21: This slide provides information regarding natural language processing technique.
Slide 22: This slide denotes information regarding the functioning of the sentiment analysis approach while detecting the core message of the text.
Slide 23: This slide delientes information regarding rule-based approach that helps in detection, classification, and scoring certain keywords based on pre-defined lexicons.
Slide 24: This slide marks provides information regarding essential steps included in the rule-based approach in NLP.
Slide 25: This slide contains information regarding segmentation technique during data-preprocessing in rule-based approach.
Slide 26: This slide caters to information regarding tokenization technique during data-preprocessing in rule-based approach.
Slide 27: This slide consists information regarding stop-word removal technique during data-preprocessing in rule-based approach.
Slide 28: This slide covers information regarding stemming and lemmatization techniques during functionalities of rule-based approach.
Slide 29: This slide highlights information regarding speech and named-entity tagging techniques during functionalities of rule-based approach.
Slide 30: This slide is an introductory slide.
Slide 31: This slide illustrates information regarding machine learning approach for performing sentiment analysis.
Slide 32: This slide puts information regarding the functioning of sentiment analysis techniques with machine learning to assess the polarity of text.
Slide 33: This slide is in continuation with the previous slide.
Slide 34: This slide highlights information regarding ML-based algorithms for sentiment analysis that enable machines to learn patterns from data.
Slide 35: This slide is an introductory slide.
Slide 36: This slide provides information regarding deep learning technology that aids systems to learn from large datasets to generate accurate insights.
Slide 37: This slide is in continuation with the previous slide.
Slide 38: This slide puts information regarding comparative analysis of recurrent and convolutional neural network for sentiment analysis on various parameters.
Slide 39: This slide is an introductory slide.
Slide 40: This slide showcases information regarding hybrid approach for sentiment analysis that combines rule-based as well as machine learning algorithms.
Slide 41: This slide shows information regarding manner through which hybrid approach performs sentiment analysis.
Slide 42: This slide provides information regarding comparative analysis of hybrid, rule-based and ML-based sentiment analysis approaches.
Slide 43: This slide is an introductory slide.
Slide 44: This slide provides information regarding model training with supervised sentiment analysis in which algorithm requires labeled dataset for training purpose.
Slide 45: This slide entails information regarding classifiers associated with supervised sentiment analysis by detecting values for parameters.
Slide 46: This slide mentions information regarding comparison of supervised and unsupervised sentiment analysis on various parameters.
Slide 47: This slide provides information regarding comparison of supervised and unsupervised sentiment analysis.
Slide 48: This slide is an introductory slide.
Slide 49: This slide showcases information regarding unsupervised learning approaches for sentiment analysis that do not require labeled training data.
Slide 50: This slide shows information regarding lexicon-based sentiment analysis approach that helps in extracting emotional polarity of text.
Slide 51: This slide is an introductory slide.
Slide 52: This slide provides information regarding aspect-based sentiment analysis is text-assessment technique.
Slide 53: This slide puts information regarding internal data collection in aspect-based sentiment analysis.
Slide 54: This slide eloborates information regarding external and API data collection in aspect-based sentiment analysis.
Slide 55: This slide pertains to information regarding various steps required to process aspect-based sentiment analysis.
Slide 56: This slide is in continuation with the previous slide.
Slide 57: This slide provides information regarding use case of aspect-based sentiment analysis in terms of fine-graining of product feedback.
Slide 58: This slide shows information regarding use case of aspect-based sentiment analysis in terms of automated customer support tasks.
Slide 59: This slide is an introductory slide.
Slide 60: This slide provides information regarding essential Python libraries utilized for sentiment analysis implementation.
Slide 61: This slide carters to information regarding various methods through sentiment analysis.
Slide 62: This slide provides information regarding various methods through sentiment analysis can be performed with Python by using bag or words vectorization-based models, and LSTM-based models.
Slide 63: This slide is an introductory slide.
Slide 64: This slide mentions information regarding real-world use case of sentiment analysis in terms of insights generation and decision making.
Slide 65: This slide highlights information regarding popular use case of sentiment analysis in managing response management in call centre.
Slide 66: This slide illustrates information regarding the popular application of sentiment analysis in managing brand reputation.
Slide 67: This slide provides information regarding the popular application of sentiment analysis in product design and improvement.
Slide 68: This slide entails information regarding the popular application of sentiment analysis in tracking customer satisfaction.
Slide 69: This slide puts information regarding the popular application of sentiment analysis in stock price prediction.
Slide 70: This slide depicts information regarding customer feedback management with sentiment analysis by gaining deeper insights from customer feedback across various feedback channels.
Slide 71: This slide is an introductory slide.
Slide 72: This slide demonstrates information regarding comparative assessment of various sentiment analysis platforms.
Slide 73: This slide elaborates on the information regarding performance assessment metrics related with the sentiment analysis-based model.
Slide 74: This slide contains information regarding the performance dashboard to analyze relevant end-user’s sentiments.
Slide 75: This slide marks information regarding the performance dashboard to analyze relevant end-user’s sentiments.
Slide 76: This slide is an introductory slide.
Slide 77: This slide highlights information regarding sentiment analysis market insights in terms of market size.
Slide 78: This slide illustrates information regarding future trends in sentiment analysis in terms of advances in deep learning and multi-modal integration.
Slide 79: This slide mentions information regarding future trends in sentiment analysis in terms of explainable AI and real-time assessment.
Slide 80: This slide shows all the icons included in the presentation.
Slide 81: This slide is titled Additional Slides for moving forward.
Slide 82: This slide is a financial slide. Show your finance-related stuff here.
Slide 83: This slide showcases Magnifying Glass to highlight information, specifications, etc.
Slide 84: This slide is a Quotes slide to convey messages, beliefs, etc.
Slide 85: This slide shows SWOT describing- Strength, Weakness, Opportunity, and Threat.
Slide 86: This slide shows Post-It Notes. Post your important notes here.
Slide 87: This slide depicts a Venn diagram with text boxes.
Slide 88: This slide is a thank-you slide with address, contact numbers, and email address.