Stock Market Trend Prediction using sentiment analysis Leveraging machine learning and sentiment analysis, we accurately forecast stock market trends. Our project combines advanced algorithms like BERT and Naïve Bayes with sentiment analysis from Twitter and other sources. By analyzing sentiment and historical price data, we provide insights

The aim of this project is to provide detailed insights into different movies analyzed focusing on the characters, their dialogues, scene locations, emotional and sentiment analysis of the whole movie and the individual characters, character's interaction with one another and finally gender distribution in the each movie analyzed.

Automated cryptocurrency analysis and reporting tool using Python. It monitors market trends, analyzes data from CoinPaprika and CryptoNews APIs, and generates weekly reports with insights. The script integrates sentiment analysis with GPT-4 and sends results via email, making it easy to track market movements.

Analyze financial news sentiment and its correlation with stock market movements. Use NLP, sentiment analysis, and financial analytics to uncover insights for enhanced financial forecasting and innovative investment strategies.

Sentiment Analysis with Insights using NLP and Dash This project show the sentiment analysis of text data using NLP and Dash. I used Amazon reviews dataset to train the model and further scrap the reviews from Etsy.com in order to test my model. Prerequisites: Python3 Amazon Dataset (3.6GB) Anaconda How this project was made? This project has been built using Python3 to help predict the sentiments with the help of Machine Learning and an interactive dashboard to test reviews. To start, I downloaded the dataset and extracted the JSON file. Next, I took out a portion of 7,92,000 reviews equally distributed into chunks of 24000 reviews using pandas. The chunks were then combined into a single CSV file called balanced_reviews.csv. This balanced_reviews.csv served as the base for training my model which was filtered on the basis of review greater than 3 and less than 3. Further, this filtered data was vectorized using TF_IDF vectorizer. After training the model to a 90% accuracy, the reviews were scrapped from Etsy.com in order to test our model. Finally, I built a dashboard in which we can check the sentiments based on input given by the user or can check the sentiments of reviews scrapped from the website. What is CountVectorizer? CountVectorizer is a great tool provided by the scikit-learn library in Python. It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. This is helpful when we have multiple such texts, and we wish to convert each word in each text into vectors (for using in further text analysis). CountVectorizer creates a matrix in which each unique word is represented by a column of the matrix, and each text sample from the document is a row in the matrix. The value of each cell is nothing but the count of the word in that particular text sample. What is TF-IDF Vectorizer? TF-IDF stands for Term Frequency - Inverse Document Frequency and is a statistic that aims to better define how important a word is for a document, while also taking into account the relation to other documents from the same corpus. This is performed by looking at how many times a word appears into a document while also paying attention to how many times the same word appears in other documents in the corpus. The rationale behind this is the following: a word that frequently appears in a document has more relevancy for that document, meaning that there is higher probability that the document is about or in relation to that specific word a word that frequently appears in more documents may prevent us from finding the right document in a collection; the word is relevant either for all documents or for none. Either way, it will not help us filter out a single document or a small subset of documents from the whole set. So then TF-IDF is a score which is applied to every word in every document in our dataset. And for every word, the TF-IDF value increases with every appearance of the word in a document, but is gradually decreased with every appearance in other documents. What is Plotly Dash? Dash is a productive Python framework for building web analytic applications. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python. Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready. Dash is an open source library, released under the permissive MIT license. Plotly develops Dash and offers a platform for managing Dash apps in an enterprise environment. What is Web Scrapping? Web scraping is a term used to describe the use of a program or algorithm to extract and process large amounts of data from the web. Running the project Step 1: Download the dataset and extract the JSON data in your project folder. Make a folder filtered_chunks and run the data_extraction.py file. This will extract data from the JSON file into equal sized chunks and then combine them into a single CSV file called balanced_reviews.csv. Step 2: Run the data_cleaning_preprocessing_and_vectorizing.py file. This will clean and filter out the data. Next the filtered data will be fed to the TF-IDF Vectorizer and then the model will be pickled in a trained_model.pkl file and the Vocabulary of the trained model will be stored as vocab.pkl. Keep these two files in a folder named model_files. Step 3: Now run the etsy_review_scrapper.py file. Adjust the range of pages and product to be scrapped as it might take a long long time to process. A small sized data is sufficient to check the accuracy of our model. The scrapped data will be stored in csv as well as db file. Step 4: Finally, run the app.py file that will start up the Dash server and we can check the working of our model either by typing or either by selecting the preloaded scrapped reviews.

Web Scraper + Sentiment Analysis + Notifying System = Social Sensor! 🕵️‍♂️📊📲 Get instant alerts about your online mentions. Manage your digital presence with AI insights! 🚀 #SocialSensor #AI #NLP

Stock Sentinel is a web app providing stock market investors with sentiment analysis and news aggregation. It uses FinBERT for sentiment analysis and offers stock information, similar stocks suggestions, summaries, and news stories. With real-time insights, it helps users make informed investment decisions. Installation is easy with cloning, depend

A sentiment analysis application with an inbuilt bot that scrapes the comment section of a twitter post for generating interactive customer review insights.

Power BI dashboard analyzing employee satisfaction survey results, combining structured survey data with NLP-based insights. Sentiment analysis is performed on open-ended comments using VADER’s SentimentIntensityAnalyzer, and topic modeling is conducted with BERTopic to identify recurring themes in employee feedback.

TKstock uses multi-model AI to analyze markets and generate smart investment insights—helping you trade with confidence. Key Features: • AI-powered fundamental/technical analysis • Real-time news & sentiment tracking • Clear, actionable recommendations

Automated sentiment analysis system using NLP, web scraping, YouTube API, and Hugging Face. Classifies user comments into positive, negative, or neutral, empowering businesses and content creators with valuable insights for informed decisions, improved satisfaction, and enhanced engagement.

This is a data pipeline project that combines historical stock price data from Nairobi Stock Exchange API with real-time news sentiment analysis to predict future stock prices for Safaricom(Company I chose). By leveraging machine learning techniques, it offers insights into market trends and helps in making informed decisions.

A PHP based solution for analyzing employee feedback and performance data with sentiment analysis, KPI tracking, and actionable insights.

I employed NLP techniques to evaluate user feedback on ChatGPT, utilizing Python libraries like VADER for sentiment analysis to categorize reviews into positive, neutral, and negative sentiments. Implemented data preprocessing techniques such as tokenization and stopword removal, visualizing results with Plotly to yield actionable insights.

FreshRSS AI Summarizer processes and summarizes articles from FreshRSS feeds using AI models. Supports OpenAI API or local Ollama models with minimal edits. Extracts key insights, sentiment analysis, and market impact, generating individual and consolidated summaries. Designed for analysts and RSS users seeking AI-driven news insights locally.

Unlock real-time insights with this comprehensive guide to building an end-to-end data pipeline. Leverage TCP/IP sockets for data ingestion, Apache Spark for powerful processing, OpenAI's LLM (ChatGPT) for sentiment analysis, and seamlessly integrate Kafka & Elasticsearch for scalable storage and querying.

This project aims to reflect the basic prototype of a climate terminal that consists of mainly 4 parts. The first part, Carbon stats, aims to reflects the overall metrics that could map and track the metrics associated with Carbon, be it Carbon emission, carbon price, carbon footprint, carbon pricing. In the prototype, we have only shown Carbon per part billion for this specific module. In the second part, we aim to target the Asset class where our aim is to provide an essence effect and description of asset class that are directly linked with climate change. For the prototype, we targeted NEw York Oil and Well Production. The third module aims to capture the Financial Market pertaining to Climate and Green Bonds and try to provide the insights related to Climate Market. In our prototype, for the sake of simplicity, we just showed forex inidicators. The tool for this specific module is intuitive. We also tried to expand the scope where user can even buy or sell stocks. Finally for the fourth tab, we focused on media sentiments, for which we developed twitter sentiment analysis tool that shows the Number of tweets, total number of words and overall sentiment score for the hot hashtags pertaining to climate change.

Abstract Depression brings significant challenges to the overall global public health. Each day, millions of people suffered from depression and only a small fraction of them undergo proper treatments. In the past, doctors will diagnose a patient via a face to face session using the diagnostic criteria that determine depression such as the Depression DSM-5 Diagnostic Criteria. However, past research revealed that most patients would not seek help from doctors at the early stage of depression which results in a declination in their mental health condition. On the other hand, many people are using social media platforms to share their feelings on a daily basis. Since then, there have been many studies on using social media to predict mental and physical diseases such as studies about cardiac arrest (Bosley et al., 2013), Zika virus (Miller, Banerjee, Muppalla, Romine, & Sheth, 2017), prescription drug abuse (Coppersmith, Dredze, Harman, Hollingshead, & Mitchell, 2015) mental health (De Choudhury, Kiciman, Dredze, Coppersmith, & Kumar, 2016) and studies particularly about depressive behavior within an individual (Kiang, Anthony, Adrian, Sophie, & Siyue, 2015). This research particularly focuses on leveraging social media data for detecting depressive thoughts among social media users. In essence, this research incorporated text analysis that focuses on drawing insights from written communication in order to conclude whether a tweet is related to depressive thoughts. This research produced a web application that performs a real-time enhanced classification of tweets based on a domain-specific lexicon-based method, which utilizes an improved dictionary that consists of depressive and non-depressive words with their associated orientations to classify depressive tweets. Problem understanding or Business Understanding Depression is the main cause of disability worldwide (De Choudhury et al., 2013). Statistically, an estimation of nearly 300 million people around the world suffers from depression. Shen et al (2017) mentioned that approximately 70% of people with early stages of depression would not consult a clinical psychologist. Many people are utilizing social media sites like Facebook and Instagram to disclose their feelings. This research persists the hypothesis that there are similarities between the mental state of an individual and the sentiment of their tweets and investigated the potentiality of social media (like twitter) as a data source for classifying depression among individuals.

An Intelligent Multi-Modal Framework for predicting student burnout via temporal sentiment analysis and behavioral pattern tracking. A full-stack solution built with Python, Flask, and NLTK to provide data-driven wellness insights and proactive mental health assessments.

A deep learning platform that combines FinBERT-based sentiment analysis with LSTM-driven stock forecasting, delivering real-time, API-powered financial insights with confidence scoring.

A web application that combines stock data analysis with news sentiment evaluation using a mixture-of-agents approach, providing insights regarding a chosen stock. If you are lazy to set this up yourself, feel free to visit https://stock-news-analysis.streamlit.app/ to explore the web application hosted using Streamlit.

An interactive Streamlit dashboard offering insights into hypothetical portfolios with features like asset overview, historical performance, sentiment analysis, and predictive forecasting. All data is illustrative

Full-stack data analytics with API integrations, sentiment and trend analysis & real-time dashboards aggregating social media data (X, FB, Inst, Discord, Medium, Reddit, YT, TT) for marketing insights

✨ SentimentSpectrum is a Flask-based web application for real-time sentiment analysis 🕒 of eCommerce reviews 🛒 using a BERT-based model 🤖. It delivers insights with labels like Positive 😊, Negative 😡, and Neutral 😐, for impactful feedback analysis. 🚀

We would like you to track and analyse the election chatter that happens in twitter, facebook and other social media channels, news sites and portals. Analyse trends and patterns and even predict the outcome of these elections. Choose a problem within the domain of 2018 assembly elections. For example, you may use different data collection methods as needed and collect different opinions from influencers and key opinion leaders on social media and analyse the sentiment of the voters. Or, you may choose to check the veracity of the opinion poll and exit poll data done by popular news channels by applying statistical concepts learnt. Whatever the problem you pick within the bounds of assembly elections, you are expected to leverage data visualization techniques learnt in the class room. Explore the data using visualization and do the first cut analysis and then deeper analysis. Apply text analytics to do various NLP tasks that help you derive election insights from social media and beyond. You can also run “Google Trends” to see the relevant trends on different elections for different time periods. Incorporate the trends in conjunction with the chatter from the media and do text analytics. Even you may do some big data analysis. You are welcome to choose any publicly available dataset of tweets, trends and posts. These questions are to generate curiosity in you.

🤖 Finalytics is an AI-driven backend for cryptocurrency technical analysis. Built with Python FastAPI and hosted on AWS, it offers candlestick pattern analysis, social media sentiment insights, and GPT-based YouTube video summaries. 📈🚀

Twitter Sentiment Analysis with Naive Bayes Algorithm. You can gain insights about Naive Bayes Algorithm and various Exploratory Data Analysis techniques.

🌿 AI-powered environmental news aggregation & analysis platform with smart query expansion, sentiment balancing, and real-time insights.

SmartNews Analyzer is an AI-driven web tool built with Streamlit that empowers users to search, analyze, and extract insights from news articles and YouTube videos. Leveraging APIs from NewsAPI and Google Cloud Console (YouTube API), this system provides deep sentiment analysis, visualization, and summarization of news content.

Stock Scanner with Local LLM for Sentiment Analysis - McMaster Engineering Capstone 2025. Real-time stock market scanner with AI-driven sentiment analysis using a local LLM. Analyze news, social media for trading insights. Custom technical indicators, privacy-focused, user-friendly. Ideal for investors, traders, developers in AI finance.