**INTRODUCTION** **PROBLEM STATEMENTLITERATURE REVIEW /** **EXISTING SOLUTIONSPROPOSED SOLUTION** **FEASIBILITY** **DESIGN OF SYSTEM** future scope give all this info according to \*Title of the Project:\*\* SentiSense — Sentiment Analysis Using Machine Learning \## 1. INTRODUCTION \### 1.1 Overview of Project Domain (Machine Learning) Machine Learning (ML) is a branch of Artificial Intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. In the field of Natural Language Processing (NLP), ML is widely used to understand, interpret, and analyse human language. One of the most important applications of NLP and ML is \*\*Sentiment Analysis\*\*, also known as Opinion Mining. It is the automated process of detecting and classifying emotions or opinions in text data. With the explosion of digital content — social media posts, product reviews, news articles, and customer feedback — sentiment analysis has become a critical tool for businesses, researchers, and developers. \### 1.2 Problem Statement In today's digital world, vast amounts of text data are generated every second across platforms like Twitter, Amazon, YouTube, and news websites. Manually reading and categorising this data as positive, negative, or neutral is practically impossible due to the volume. There is a need for an automated, intelligent system that can accurately classify the sentiment of any given text in real time. \### 1.3 Objectives of the Project The objectives of this project are: 1\. To build a Machine Learning model capable of classifying text sentiment as Positive, Negative, or Neutral. 2\. To preprocess and vectorise text data using NLP techniques (tokenisation, stopword removal, TF-IDF). 3\. To train and evaluate a Logistic Regression classifier on a real-world dataset. 4\. To deploy the trained model as an interactive web application using Flask. 5\. To provide users with confidence scores for each prediction. \### 1.4 Scope of the Project \- The system accepts any English text input (reviews, comments, sentences). \- It classifies sentiment into three categories: Positive, Negative, and Neutral. \- Neutral is detected when the model's confidence falls below 58%, indicating uncertainty. \- The system is accessible through a web browser without requiring any technical knowledge. \- The project covers core ML concepts: data preprocessing, feature engineering, model training, evaluation, and deployment. \--- \## 2. LITERATURE SURVEY / EXISTING SYSTEM \### 2.1 Brief Study of Existing Methods Several approaches have been used for sentiment analysis in research and industry: \| Method | Description | Limitation | \|--------|-------------|------------| \| \*\*Lexicon-Based\*\* | Uses a dictionary of positive/negative words (e.g., VADER, TextBlob) | Cannot understand context; limited to predefined word lists | \| \*\*Naive Bayes\*\* | Probabilistic classifier; fast and simple | Assum
**INTRODUCTION** **PROBLEM STATEMENTLITERATURE REVIEW /** **EXISTING SOLUTIONSPROPOSED SOLUTION** **FEASIBILITY** **DESIGN OF SYSTEM** future scope give all this info according to \*Title of the Project:\*\* SentiSense — Sentiment Analysis Using Machine Learning \## 1. INTRODUCTION \### 1.1 Overview of Project Domain (Machine Learning) Machine Learning (ML) is a branch of Artificial Intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. In the field of Natural Language Processing (NLP), ML is widely used to understand, interpret, and analyse human language. One of the most important applications of NLP and ML is \*\*Sentiment Analysis\*\*, also known as Opinion Mining. It is the automated process of detecting and classifying emotions or opinions in text data. With the explosion of digital content — social media posts, product reviews, news articles, and customer feedback — sentiment analysis has become a critical tool for businesses, researchers, and developers. \### 1.2 Problem Statement In today's digital world, vast amounts of text data are generated every second across platforms like Twitter, Amazon, YouTube, and news websites. Manually reading and categorising this data as positive, negative, or neutral is practically impossible due to the volume. There is a need for an automated, intelligent system that can accurately classify the sentiment of any given text in real time. \### 1.3 Objectives of the Project The objectives of this project are: 1\. To build a Machine Learning model capable of classifying text sentiment as Positive, Negative, or Neutral. 2\. To preprocess and vectorise text data using NLP techniques (tokenisation, stopword removal, TF-IDF). 3\. To train and evaluate a Logistic Regression classifier on a real-world dataset. 4\. To deploy the trained model as an interactive web application using Flask. 5\. To provide users with confidence scores for each prediction. \### 1.4 Scope of the Project \- The system accepts any English text input (reviews, comments, sentences). \- It classifies sentiment into three categories: Positive, Negative, and Neutral. \- Neutral is detected when the model's confidence falls below 58%, indicating uncertainty. \- The system is accessible through a web browser without requiring any technical knowledge. \- The project covers core ML concepts: data preprocessing, feature engineering, model training, evaluation, and deployment. \--- \## 2. LITERATURE SURVEY / EXISTING SYSTEM \### 2.1 Brief Study of Existing Methods Several approaches have been used for sentiment analysis in research and industry: \| Method | Description | Limitation | \|--------|-------------|------------| \| \*\*Lexicon-Based\*\* | Uses a dictionary of positive/negative words (e.g., VADER, TextBlob) | Cannot understand context; limited to predefined word lists | \| \*\*Naive Bayes\*\* | Probabilistic classifier; fast and simple | Assum
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This project focuses on sentiment analysis in natural language processing (NLP), automating the classification of sentiments as positive, negative, or neutral, and emphasizing its impact on business through review volume. It proposes a system that compares lexicon-based and machine learning methods, utilizing TF-IDF with Logistic Regression for predictions, deployed via Flask with confidence scores. The feasibility assessment covers technical, operational, and economic aspects, while future...