# shirosaidev/stocksight

Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis

Repository: https://github.com/shirosaidev/stocksight
Canonical: https://ross.abutalabs.com/products/stocksight
Homepage: https://shirosaidev.github.io/stocksight/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: stock-market, stock-prediction, python, tweepy, sentiment-analysis, textblob, twitter-streaming-api, twitter-sentiment-analysis, nltk, natural-language-processing, twitter, vader-sentiment-analysis, stock-price-prediction, stock-analysis, stock-analyzer, sentiment, elasticsearch
Last push: 2023-12-05T06:45:25+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3264, "days_push": 1002, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2530, forks 495 (observed 2026-08-28T04:06:58.848541+00:00)

## What it is
stocksight is an open-source Python application that analyzes stock market sentiment using Twitter streams and news headlines, storing data in Elasticsearch with Kibana dashboards. It applies NLP sentiment analysis (TextBlob, VADER, NLTK) to gauge how social media and news emotions correlate with stock prices.

## Use cases
- analyze stock market sentiment from twitter and news headlines
- predict stock price movements using sentiment analysis
- store and visualize stock-related social media data in elasticsearch
- run sentiment analysis on any topic, not just stocks
- build a kibana dashboard for stock sentiment trends

## When to choose
- you want an open-source sentiment-driven stock analysis tool with elasticsearch and kibana visualization
- you need to correlate twitter/news sentiment with stock prices
- you want a docker-compose deployable sentiment analysis pipeline

## When to avoid
- you need production-grade, actively maintained trading software - development is infrequent
- you don't want to run elasticsearch and kibana infrastructure
- you need guaranteed financial accuracy - sentiment-based prediction is experimental

## Facets
- artifact type: application
- maturity: maintenance
- function: nlp, search-engine, analytics, data-science, web-scraping
- domain: fintech, analytics, data-science
- platform: python, self-hosted
- tags: sentiment-analysis, stock-market, twitter, elasticsearch, kibana, stock-prediction, news-headlines, natural-language-processing, docker, linux

## Member repositories
- shirosaidev/stocksight (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:58.848541+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:25:21.628766+00:00, confidence not recorded.
  - readme: https://github.com/shirosaidev/stocksight (fetched 2026-08-28T04:06:58.848541+00:00, sha e44cf36a3d0f)
  - homepage: https://shirosaidev.github.io/stocksight/ (fetched 2026-08-29T10:07:39.681456+00:00, sha 4482200c7ada)
- Data as of 2026-08-30T08:39:29.467469+00:00.