socialmediasentiment

App_Sentiments

This dashboard fetches CSV sentiment data from a GitHub raw URL and presents it through summary cards, an SVG bar chart, and an interactive data table. Using the Fetch API and a custom CSV parser, the application calculates aggregate metrics and renders randomized sample bars as placeholder visualizations, while CSS grid and flexbox handle the responsive layout. The chart is built with native SVG elements manipulated via JavaScript, and the table includes hover-highlighted rows for readability.

Mmeangreen0067@gmail.com

73% match

Tweet Sentiment Distribution - Predictive

This visualization shows the predicted distribution of tweet sentiments—negative, positive, and neutral—as a donut chart, with each slice labeled by its percentage. The data is loaded from a CSV file using `d3.csv` and preprocessed to calculate percent ranges. The chart is rendered with SVG paths generated by `d3.pie` and `d3.arc`, while a color scale via `d3.scaleOrdinal` maps sentiments to distinct colors. A legend created with `d3.legendColor` clarifies the color encoding, and a title is displayed above the chart.

72% match

Word Sentiment Distribution

A pie chart visualizes the distribution of sentiment categories—negative, positive, and neutral—for words in a Russia-Ukraine Twitter dataset, using d3.pie and d3.arc to generate SVG arcs with percentage labels. A d3.scaleOrdinal maps colors to sentiments, while a d3.legendColor legend identifies categories. Data loads as CSV from GitHub, and the chart emphasizes proportional percentages rather than raw counts.

71% match

Sentiment Scores of Tweets

A scatterplot displays sentiment scores for a sequence of tweets, indexed along the x-axis with AFINN-based scores on the y-axis. The visualization reveals that most tweets cluster around a neutral score of zero, with sporadic positive and negative outliers. The chart is built with D3 v3, using SVG circles, linear scales, and axes to map tweet index and sentiment score. Data is loaded from a CSV file containing tweet indices and their corresponding sentiment values.

JJverma

Axes & Labels: Social Media MQP

This visualization presents survey responses about social media usage as a diverging stacked bar chart, with each bar representing a different platform and segments colored by six-point agreement or frequency scales. The chart sorts platforms by net positive sentiment and includes labeled axes, a title, and a legend. Built with d3.v7, it uses `scaleLinear` and `scaleBand` for positioning, `axisLeft` and `axisBottom` for axes, and `schemeRdBu`-derived colors to encode responses. Data is fetched from a remote CSV and aggregated using `d3.rollup` and `d3.group`.

KK Munz

Word Sentiment Distribution - Predictive

A donut chart displays the predicted sentiment distribution of words from a Russia-Ukraine Twitter analysis dataset, breaking percentages into negative, positive, and neutral categories. The visualization uses d3.pie and d3.arc to generate the chart, with d3.scaleOrdinal mapping sentiment categories to distinct colors. Percent labels are positioned using d3.arc centroids, and a categorical legend is rendered via the d3-legend plugin's d3.legendColor. The data is loaded as CSV from a GitHub repository, and the SVG rendering uses a custom viewBox with margins and a fixed radius for the arc paths.

Front-End for IR project

This interactive visualization front-end compares Twitter and Reddit sentiment and topic associations for user-entered words. It displays sentiment as colored, neutral, positive, or negative labels alongside word-similarity and topic-term lists. Built with D3.js v6, the interface renders SVG text and labels across a divided screen, with input fields for two words. The implementation uses custom JavaScript helper functions to map sentiment values to colors and mouth-angle symbols, while layout is managed through CSS flexbox and absolute positioning in the grid container.

LLeah Mitchell

Tweet Sentiment Distribution

This visualization shows the sentiment distribution of tweets about Ukraine, displaying the percentage of tweets categorized as negative, positive, or neutral in a pie chart. The chart uses D3.js v7 with an SVG rendering approach, pulling data from a GitHub-hosted CSV file. The code employs `d3.pie()` and `d3.arc()` to generate the pie slices, `d3.scaleOrdinal` with a custom color scale for sentiment categories, and the d3-legend library to display the color legend. Percentages are shown on each slice using `d3.format(".1%")`.