AAAI18 paper 《Improving Review Representations with User Attention and Product Attention for Sentiment Classification》

A Classification Machine Learning Model for Validating Match of Product Score Rating Against its Review Comment Using Sentiment Analysis

In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.

In this project we will be classifying reviews given by the employers for the employee or the company as positive or negative reviews. The dataset contains 67,529 rows and 15 columns. The dataset has information primarily regarding the company, position, date, pros and cons. This project can help a company analyze the ratio of employees that are satisfied or not satisfied with their work environment. This can help in future improvements and help give a better experience to the future employees. Using the word cloud for positive and negative sentiment, they can better understand which problems are more precarious than the others and focus on them rather than those that don’t need immediate attention. This can also be leveraged by rival companies to understand the problems of the competition to avoid those themselves. The positive views can also be used extensively to understand why the competition may be prospering and can be incorporated into a company’s work culture for a holistic work experience. Using sentiment analysis on reviews of any kind can help in understanding the deep-seated issues with a product or a workplace and can also be used to optimize on all the things that are going right and strive towards excellence. Steps: Data collection: the first step of sentiment analysis consists of collecting data from user generated content contained in blogs, forums, social networks and text analytics and natural language processing are used to extract and classify. In our case it is collected from Kaggle. Text preparation: consists of cleaning the extracted data before analysis. We will be using techniques such as bag of words and lemmatization. Feature Extraction: the extracted sentences of the reviews and opinions are examined. Use word embedding (count vectorizer, tf-idf transformation, Word2Vec) to transform reviews into numerical representations. Machine learning classifier: Fit numerical representations of reviews to machine learning algorithms. We will be using Naïve Bayes, Logistic Regression, Random Forest and LSTM. Sentiment classification: Subjective sentences are classified in positive, negative, good or bad. Presentation of output: the main objective of sentiment analysis is to convert unstructured text into meaningful

A project that develops a sentiment analysis model using transfer learning techniques for effective cross-domain sentiment classification. The model can be trained on one domain (e.g., product reviews) and then adapted to perform well on a different domain (e.g., social media posts) with minimal labeled target data.

Sentiment analysis also known as opinion mining is a subfield within Natural Language Processing (NLP) that builds machine learning algorithms to classify a text according to the sentimental polarities of opinions it contains, e.g., positive or negative. In recent year, sentiment analysis has become a topic of great interest and development in both academics and industry. Analysing the sentiment of texts could benefit, for example, customer services, product analytics, market research etc. Take Ebay as an example. Customers on Ebay choose their preferred products based on the reviews from other users. an automatic sentiment classification system can not only help companies grasp the satisfaction level of the products, but also significantly assist new customers to locate their online shopping shelves. In this data analysis challenge, we are interested in developing such an automatic sentiment classification system that relies on machine learning techniques to learn from a large set of product reviews provided by Yelp. The levels of polarity of opinion we consider include strong negative, weak negative, neutral, weak positive, and strong positive. For example, “Website says open, Google says open, Yelp says open on Sundays. Our delivery was cancelled suddenly and no one is answering the phone. Shame” gives us a strong negative sentiment, whereas the sentiment of “They have great food & definitely excellent service. Tried their mochi mango flavored and it s definitely delis” is likely to be strong positive. The sentiment analysis task is often formulated as a classification problem, where a classifier is fed with a text and returns the corresponding sentiment label, e.g., positive, negative, or neutral. In other words, the problem of learning the sentimental polarities of opinions is reduced to a classi- fication problem. There are many machine learning methods that can be used in the classification task. They can be categorised into supervised method (like SVM) and unsupervised method (like clustering).

Integration of a trained sentiment classification model into a Flask web app for real-time inference on product reviews from Flipkart store.

I built Sentiment Analysis models leveraging a deep learning approach utilizing the customer reviews of Amazon products. Since Long Short Term Memory Network (LSTM) is very effective in dealing with long sequence data and learning long-term dependencies, I used it for automatic sentiment classification of future product reviews.

This project is a machine learning-based review classification system that predicts whether a product review is GENUINE or FAKE. It preprocesses review text, analyzes sentiment, and uses numerical features like ratings and helpfulness to make predictions. The model is deployed via a Flask web application for user interaction.

Machine-learning classification projects for logistic regression and predicting sentiments from a product review.

Fine-tuning DistilBERT for sentiment classification on Amazon product reviews. A course project implementing text preprocessing, model training, and evaluation with Accuracy, Macro-F1, and ROC-AUC.

The problem of predicting a user's star rating for a product, given the user's text review for that product, is called Review Rating Prediction and has lately become a popular problem in machine learning. In this project, we implement an approach which involves a combination of topic modeling and sentiment analysis to achieve this objective by treating Review Rating Prediction as a multi-class classification problem, and building different prediction models by using Latent Dirichlet Allocation as the underlying feature extraction method with three machine learning algorithms, (i) K Nearest Neighbors, (ii) Multinomial Naive Bayes and (iii) Random Forest. We analyze the performance of each of these models to come up with the best model for predicting the ratings from reviews. We use the dataset provided by Yelp for training and testing the models.

Convolutional neural networks (CNN) was used in order to analyze the sentiment of the customer based on his product reviews. convolutional neural network (CNN, or ConvNet) is a class of ​ Supervised deep neural networks​ , most commonly applied to analyzing visual imagery and text classification. RNN is also one of the Supervised Deep Neural Networks which can be used to analyze the sentiments of the customer. Many-to-One Model is the RNN architecture used for Sentimental Analysis.

This project's actual concept is to automatically suggest user-preferred videos based on emotion detection and sentiment analysis. In the existing system, users have to select the videos manually. Randomly played videos may not match the mood of the user. Users have to classify the videos into various emotions for playing the videos. By using the ESV system, these difficulties can no longer take place. The ESV system will use a deep learning technique Convolutional Neural Network (CNN) algorithm for emotion detection. CNN is used to solve classification or identification problems. For feedback, the ESV system will use a sentiment analysis algorithm. As reviews only rank the app or product. But with sentiment analysis, we will suggest the best-recommended videos to our customers as sentiment analysis helps us understand a sneak peek of our customers' emotions. The software requires the user to have a profile to access the application. The user needs to grant permission for the application to access the device's camera and media. This application saves the user profile on the device and keeps the profile logged-in until a user logs out of the device manually. As soon as the user opens the application, the device's camera opens and captures his facial expressions. The ESV system will determine emotions and suggest the best-recommended videos that may fit their current mood. The recommendations are according to user emotion and sentiment.

Cross Lingual Sentiment Classification for Amazon Product Reviews

Text classification for sentiment analysis with Amazon reviews, focusing on kitchen products.

Product rating review analysis considers the sentiment classification problem for online reviews using supervised approaches .

Sentiment-based binary classification model (positive/ negative) for customer product review on amazon

Amazon Product Reviews dataset chosen for diverse opinions & product categories. Objective: Build classification model using text & other factors to predict review sentiment (pos/neg).

A comprehensive Natural Language Processing (NLP) project comparing classical and modern approaches for sentiment classification on product reviews.

Using Bidirectional-Attention-LSTM and ARIMA for Amazon Product Review Sentiment Classification and Time Series Analysis

Analysing Sentiments or Reviews given by customers using different classification Algorithms for forecasting the Product demand by using Time series analysis

Fine-tuned BERT model for sentiment classification on Amazon product reviews. Classifies reviews as positive, neutral, or negative. Built using Hugging Face Transformers and trained by Sara Moshtaghi.

Comparative NLP project for sentiment classification. Implements and evaluates rule-based (VADER) and fine-tuned Transformer (RoBERTa) models on Amazon Product Review data.

A sentiment classification pipeline for Persian-language product reviews using Word2Vec embeddings and logistic regression. The project includes text preprocessing, embedding, model training, and prediction on unseen test data.

Machine Learning & Natural Language Processing - A sentiment classification system for product reviews using Python, ML, and NLP, which features a modular, extensible architecture based on the Strategy Design Pattern.

This repository consists of essential files for running InsightRush, an AI-powered web app for analyzing Amazon and Flipkart product reviews. It includes sentiment classification models (CNN, LSTM, BERT), review summarization, and MySQL database integration.

The project involves building a classification machine learning pipeline to predicts the sentiment of Amazon product reviews using trained classification models. The objectives include implementing multiple classifiers, evaluating performance using standard metrics, and creating a user-friendly web application for classification analysis.

This repository contains code for a sentiment-based product recommendation system implemented using machine learning and collaborative filtering techniques. It includes modules for sentiment analysis of user reviews, building and evaluating machine learning classification models, and developing collaborative filtering recommendation systems.

🛍️ SentiMart BERT-powered sentiment analysis system for Amazon product reviews with interactive dashboard and real-time processing capabilities achieving 90%+ accuracy. Tech Stack: Python • PyTorch • BERT • Streamlit • Scikit-learn Features: Sentiment classification • Real-time processing • Interactive dashboard • Business metrics