III-II_Year_AD_review_1.pptx HVGHFGGYVYTUU

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1. BACHELOR OF TECHNOLOGY IN Artificial Intelligence and Machine Learning Batch Number : Project Guide: Batch Names & Roll Numbers Department of AIML, School of Engineering Malla Reddy University Hyderabad. ZT-9 B.Raju - 2211CS020656 T.Devender -2211CS020657 P.Jagdeesh -2211CS020658 M.Bharath kumar -2211CS020659 V.Shreyan -2211CS020660 Prof : K.Divya Bharathi

2. SENTIMENT ANALYSIS FOR SMATER MARKETING

3. CONTENTS INTRODUCTION: Project Identification / Problem Definition Objective of project Scope of the project ANALYSIS: Project Planning and Research Software requirement specification Software requirement Hardware requirement Model Selection and Architecture DESIGN: Introduction DFD/ER/UML diagram(any other project diagram) Data Set Descriptions Data Preprocessing Techniques Methods & Algorithms 

4. Introduction In the digital age, customer opinions shape brand reputation and market success. Sentiment analysis helps businesses understand consumer emotions, detect trends, and optimize marketing strategies Sentiment analysis, a key NLP application, helps businesses extract meaningful insights from customer feedback, product reviews, and social media discussions. This project leverages advanced NLP techniques to analyze sentiment, detect emotions, and provide data-driven marketing strategies. P roblem Statement Businesses face challenges in accurately interpreting customer sentiments due to language diversity, fake reviews, and unstructured data. Traditional methods fail to capture deep emotions and intent, leading to ineffective marketing decisions. This project aims to overcome these limitations using NLP-based sentiment analysis, enabling precise emotion detection, fake review identification, and sentiment-driven marketing strategies and predictive insights for better customer engagement and retention.

5. Objectives Develop an NLP-based sentiment analysis system to classify customer feedback as positive, neutral, or negative. Support multi-language sentiment analysis to analyze feedback from diverse user demographics. Detect emotions in product reviews to understand customer satisfaction and engagement levels. Identify and filter out fake reviews to ensure the authenticity of insights. Provide sentiment-driven recommendations and predictive analytics for effective marketing strategies. Ad Generating Based on Sentiments

6. ANALYSIS Project Planning: Defining project objectives, scope, and key deliverables. Gathering and preprocess datasets from social media, e-commerce platforms, and review sites. Selecting appropriate NLP models and machine learning techniques for sentiment analysis. Developing and test the sentiment classification system with real-world data. Integrate the system into a dashboard for visualization and insights. Research: Study existing sentiment analysis techniques using NLP, including machine learning and deep learning models (VADER, BERT, RoBERTa ). Explore methods for multi-language sentiment analysis and emotion detection. Analyze techniques for fake review detection and authenticity verification. Review industry use cases of sentiment-driven marketing strategies. Evaluate sentiment-based predictive analytics for customer retention and targeted advertising.

7. ARCHITECTURE

8. DATA FLOW DIAGRAM

9. Dataset Description: The dataset consists of customer reviews, social media comments, and e-commerce feedback in multiple languages. It includes labeled sentiments: Positive, Negative, Neutral and emotional states (e.g., Happy, Angry, Sad). Additional metadata such as timestamps, user ratings, and product categories for deeper insights. Data Preprocessing Techniques: Text Cleaning : Removing special characters, numbers, and unnecessary whitespace. Tokenization : Splitting text into individual words or phrases for analysis. Stopword Removal : Eliminating common words (e.g., "the," "is") that do not contribute to sentiment. Lemmatization/Stemming : Converting words to their root form (e.g., "running" → "run"). Word Embeddings : Using TF-IDF, Word2Vec, or BERT embeddings for better context understanding. Handling Imbalanced Data : Using SMOTE (Synthetic Minority Over-sampling Technique) or class weighting for balanced sentiment classification.

10. Methods : Natural Language Processing (NLP): For text analysis, tokenization, and word embeddings. Supervised Machine Learning: For sentiment classification and fake review detection. Deep Learning Models: To improve sentiment accuracy and handle multi-language inputs. Anomaly Detection: To identify and filter out fake or misleading reviews. Algorithms : Sentiment Classification: Logistic Regression, Support Vector Machine (SVM), Random Forest (Baseline models). BERT & RoBERTa : For context-aware, multi-language sentiment analysis. Fake Review Detection: Random Forest, XGBoost , and Neural Networks to detect spam patterns in reviews. Emotion Detection: Multi-label classification with BERT/LSTM to detect emotions (e.g., Happy, Angry, Sad). Predictive Analytics & Sentiment-Driven Recommendations: Time Series Forecasting (ARIMA, LSTMs) to predict customer sentiment trends. Collaborative Filtering & Content-Based Filtering for sentiment-driven recommendations.

11. Thank you