Code for A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural Models

Twitter tweets play an important role in every organisation. This project is based on analysing the English tweets and categorizing the tweets based on the sentiment and emotions of the user. The literature survey conducted showed promising results of using hybrid methodologies for sentiment and emotion analysis. Four different hybrid methodologies have been used for analysing the tweets belonging to various categories. A combination of classification and regression approaches using different deep learning models such as Bidirectional LSTM, LSTM and Convolutional neural network (CNN) are implemented to perform sentiment and behaviour analysis of the tweets. A novel approach of combining Vader and NRC lexicon is used to generate the sentiment and emotion polarity and categories. The evaluation metrics such as accuracy, mean absolute error and mean square error are used to test the performance of the model. The business use cases for the models applied here can be to understand the opinion of customers towards their business to improve their service. Contradictory to the suggestions of Google’s S/W ratio method, LSTM models performed better than using CNN models for categorical as well as regression problems.

Source code for the Twitter Hybrid Sentiment Classifier used in Semeval 2014 competition. (Sentiment Analysis system)

Hybrid Approach for Aspect-Based Sentiment Analysis

A Hybrid Recommendation model based on sentiment analysis on tweets and item based filtering to closely match preferred recommendation.

A real-time sentiment analysis platform powered by a fine-tuned BERT model and Flask API. The application features a high-performance React frontend with interactive GSAP animations. It uses a hybrid architecture on Vercel and Hugging Face, ensuring high-accuracy binary classification with instant confidence scoring.

Customer reviews on e-commerce websites play a vital role in driving the customer purchasing behavior. Walmart currently follows an overall five-star rating system and it is not reflective of the actual sentiments expressed by the buyers and the verbal descriptive reviews do not distinctively enlighten prospective customers about the specific aspects in products or services. It is essential for Walmart to mine user reviews to extract sentiments by different aspects specific to every product (like quality, durability, warranty service, pricing, reliability etc.) and service (like shipping, packaging, return/ replacement policies etc.). This process can help draw specific insights about the product and service which will help both Walmart and potential customers to take more informed decisions.

The Data for paper "Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional Networks"

This project represents a Hybrid recommender system based on DL and sentiment analysis for my bachelor's final year In USTHB

Syntax-Directed Hybrid Attention Network for Aspect-Level Sentiment Analysis

Stock_Market_Prediction using textual analysis : Applying the NLP processing and the Sentiment analysis to the textual dataset(news headlines) * Numericel analysis : Applying the numerical analysis to the historical stock prices dataset * merging the two datasets : Create a hybrid model for stock price/performance prediction using deep learning LSTM for time series forcasting with the avoidance of overfitting

Sentiment analysis of twitter data using glove + LSTM, and a hybrid model

This is a Hybrid model for Persian Sentiment Analysis with Digikala comment dataset

ALDONAr: A Hybrid Solution for Sentence-Level Aspect Based Sentiment Analysis using a Lexicalized Domain Ontology and a Regularized Neural Attention Model

Travel Recommendation System using Python combines Collaborative Filtering (UBCF, IBCF, SVD) with Sentiment Analysis of user reviews to suggest personalized travel destinations. Includes cold-start handling, hybrid scoring, and user-friendly recommendations.

Aistox– A hybrid AI-driven system that combines financial fundamentals, technical indicators, macroeconomic data, and real-time sentiment analysis from news and social media to predict stock price movements with greater contextual accuracy.

An AI-powered financial intelligence platform, Hybrid-RAG for accurate financial term retrieval and market news summarization. It also integrated sentiment analysis for market mood insights and optimized the RAG retrieval pipeline for improved query relevance.

To extract and sentiment analysis from a verbal description, text-based sentiment detection is employed. Text-based sentiment detection categorized into two main phases, including language representation and classification. Language representation proposes a robust technique to extract the contextual 2 information from the text to increase the quality of feature extraction. Classification employed neural networks to increase classification performance. These techniques have been applied to extract sentiment from the “IMDB” movie review dataset. Three general approaches are represented to detect sentiment analysis, including Rule Construction, Machine Learning (ML), and Hybrid Approaches. Outcome: provided the text processing techniques used in NLP and different feature extraction methods, including Bag of words, TF-IDF, Word2Vec, and Glove. Demonstrated the use of text processing and build a Sentiment Analyzer with classical ML approaches that achieved fairly good results. Described in detail the architecture of the Deep Learning model for sentiment classification. Hence, trained a word2vec model and used it as a pre-trained embedding for sentiment classification. This knowledge applied to experiment with deep learning NLP models to classify film reviews as positive or negative. Some of these models involved layer types (dense and convolutional layers), while later ones involved new layer types from the RNN family (LSTMs and GRUs). In a conclusion, deep learning models offer clear comprehensibility of the extracted feature prior to classification.

SentArg: A Hybrid Doc2Vec/DPH Model with Sentiment Analysis Refinement

This is the official repository for the project: Joint Sentimental Analysis Based on Tree topology as a commitment to Chen Liao's final dissertation in University of Nottingham Ningbo China (UNNC).

Hybrid project integrating Large Language Models (LLM) for financial news sentiment analysis with Reinforcement Learning (RL) to optimize investment and portfolio strategies.

Open source code for paper: Hybrid contrastive learning of tri-modal representation for multimodal sentiment analysis

ALDONA: A Hybrid Solution for Sentence-Level Aspect Based Sentiment Analysis using a Lexicalized Domain Ontology and Neural Attention Model

Sentiment analysis of IMDB reviews using CNN, RNN, hybrid CNN+RNN, BLSTM, and BERT. Interactive web app powered by Flask API. Full-stack NLP pipeline showcasing data science, ML engineering, and web dev skills. Try the hybrid model and experience the power of advanced sentiment analysis

Developing a hybrid time-series forecasting model which incorporates the influence of external phenomenon through sentiment analysis of financial news headlines to forecast closing stock prices of bombay stock exchange.

This project aimed to predict cryptocurrency prices using LSTM analyze real-time sentiment from tweets using a hybrid LSTM+VADER model and recommend the best coin to invest in based on combined analysis

The Intelligent Prediction Market A hybrid (Fiat/Crypto) prediction market platform featuring an Automated Market Maker (CPMM) for guaranteed liquidity and an AI Co-Pilot for real-time news sentiment analysis. Built with Django, Vite.js, and Solidity.

This project predicts mood using a hybrid ML approach with NLP. It utilizes BERT for sentiment analysis, LSTM for time series, and regressor models for forecasting. Data is stored in MySQL, visualized in Tableau, and accessed via a Streamlit GUI. SHAP and LIME ensure model interpretability.

Developed a hybrid rule-based and dictionary-enhanced stemmer with over 300 morphological rules and a 50,000+ word dictionary to accurately process Punjabi language morphology. Designed to reduce overstemming and understemming errors, improving preprocessing for NLP tasks including sentiment analysis. Implemented an algorithmic flow with linguis...

More than 90% of traders lose money on stock market because they fail to sync emotions with strategy to trade .Our approach of Stock Price prediction is one the way to solve the problem DJIA index prediction with LSTM-ARIMA hybrid model and News Sentiment Analysis . Achieved accuracy rate of 98.5 % on 75-25 Train Test Split. Combined News + Stock price data is large file size of around 227 MB. If can't download here's link to kaggle :https://www.kaggle.com/aaron7sun/stocknews Access the weights of LSTM ,ARIMA models individually .\ To use ARIMA model - Use command ``` loaded = ARIMAResults.load('arima_model.pkl') ``` \ To use LSTM model - Use command ``` model = tf.keras.models.load_model('lstm_model.h5') ```