NEURAL NETWORK

Sentiment Analysis using Natural Language Processing | Python Projects for Resume

ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, NEURAL NETWORK /

Sentiment Analysis on Reviews with Neural Networks Sentiment Analysis on Reviews utilizes neural networks to analyze and interpret sentiments expressed in product or service reviews. This application processes textual content to determine whether reviews are positive, negative, or neutral, providing valuable insights for businesses and consumers. The neural networks’ ability to understand context and nuances […]

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Twitter Sentiment Analysis using Machine Learning | IEEE Based Projects for CSE

ARTIFICIAL INTELLIGENCE, DEEP LEARNING, MACHINE LEARNING, NEURAL NETWORK /

Twitter Sentiment Analysis using NLP Twitter Sentiment Analysis is a desktop based software, designed and developed using Python programming language. Aim of the tool is to analyze the sentiment of the tweets from the user. Basically it has five classes, Positive, Strong Positive, Negative, Strong Negative & Neutral. It uses LSTM, NLTK, Tokenizer, Gensim as

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Twitter Sentiment Analysis using NLP | Machine Learning Projects | Python Projects

ARTIFICIAL INTELLIGENCE, DEEP LEARNING, MACHINE LEARNING, NEURAL NETWORK /

Analyzing Twitter Sentiment with Python Sentiment Analysis is the process of determining whether a piece of word or sentense is negative, positive or neutral. The Twitter sentiment analysis model developed using python programming language and tkinter is used to create graphical user interface. Twitter Sentiment Analysis is performed to verify the sentiment towards various topic.

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Twitter Sentiment Analysis using Machine Learning Project | CSE Project Ideas for Final Year

ARTIFICIAL INTELLIGENCE, DEEP LEARNING, MACHINE LEARNING, NEURAL NETWORK /

Twitter Sentiment Analysis using Python NLP The Twitter sentiment analysis model developed using python programming language and tkinter is used to create graphical user interface. Twitter Sentiment Analysis is performed to verify the sentiment towards various topic. Collected tweets from twitter will analyzed and identify the different sentiment present in tweets using machine learning model.

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YouTube Comment Sentiment Analysis Python | Final Year Project Based on Machine Learning

DEEP LEARNING, MACHINE LEARNING, NEURAL NETWORK /

YouTube Comment Sentiment Analysis using Machine Learning YouTube Comment Sentimental Analysis in the interface it have to take the YouTube link as input and it have to display all the below objectives. YouTube Comment Sentiment Analysis Project developed using Python Programming Language and Natural Language Processing is a subfield of linguistics, artificial intelligence and computer

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Chat with PDF Files using Langchain Open AI | PDF Reading and Customer Support with AI Technology

ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, NEURAL NETWORK /

Chat with PDF Files using Langchain Open AI The rapid growth of digital documents has necessitated efficient tools for document management and reading. This paper introduces a novel PDF reader chatbot application designed to improve the user experience and interaction with PDF documents. The chatbot leverages natural language processing (NLP) and machine learning techniques to

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Image Caption Generator using CNN | Machine Learning Projects for Final Year

ARTIFICIAL INTELLIGENCE, DEEP LEARNING, IMAGE PROCESSING, MACHINE LEARNING, NEURAL NETWORK, OPENCV /

Image Caption Generator Project in Python with Source Code Image Caption Generation with Neural Networks represents a significant advancement in artificial intelligence, enabling machines to generate descriptive captions for images. User have to upload input image and model will generate text with head counts and count the number of person, Similarly it works for video

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Face Emotion Recognition using Machine Learning | Speech Emotion Recognition using Python

ARTIFICIAL INTELLIGENCE, COMPUTER VISION, MACHINE LEARNING, NEURAL NETWORK, OPENCV /

Face Emotion Recognition using Image Processing In automobiles, safety is important factor and driver factor is most efficient for road safety and it found that 90% of road accident are due to driver fault. The driver emotion plays a important role in ensuring safety while driving. Thus detecting driver emotion could improve road accident. This

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