Who should use the Analyze sentiment workflow?
Teams or solo builders working on finance & legal tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Finance & Legal
A streamlined workflow to analyze sentiment from financial market and financial data inputs. Begin by gathering market sentiment data, then collect relevant financial data, and finally perform sentiment analysis to produce actionable insights. This workflow focuses on financial sentiment analysis and excludes unrelated legal contract tasks.
Deliverable outcome
A finalized report (PDF or dashboard) with visual insights and actionable recommendations for trading or risk management.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A finalized report (PDF or dashboard) with visual insights and actionable recommendations for trading or risk management.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Brand24 to a clear scope document listing target assets and their corresponding data sources, ready for extraction. Then, you pass the output to Scrapinghub to a raw dataset of text entries (news articles, social media posts) with timestamps and source tags, ready for cleaning. Then, you pass the output to Bloomberg Terminal to a time-series dataset of financial metrics aligned with the sentiment data timeline, stored in a dataframe. Then, you pass the output to FinGPT to a clean, tokenized text corpus with domain-adapted terms, ready for sentiment scoring. Then, you pass the output to Hugging Face Spaces to a time-series of sentiment scores (e.g., daily average polarity) for each asset, with mention volume. Then, you pass the output to Thematic to a correlation matrix and lag analysis showing relationships between sentiment and market movements. Finally, Tableau AI is used to a finalized report (pdf or dashboard) with visual insights and actionable recommendations for trading or risk management.
Define Sentiment Scope and Data Sources
A clear scope document listing target assets and their corresponding data sources, ready for extraction.
Gather Market Sentiment Data
A raw dataset of text entries (news articles, social media posts) with timestamps and source tags, ready for cleaning.
Collect Relevant Financial Data
A time-series dataset of financial metrics aligned with the sentiment data timeline, stored in a DataFrame.
Clean and Preprocess Text Data
A clean, tokenized text corpus with domain-adapted terms, ready for sentiment scoring.
Execute Sentiment Analysis
A time-series of sentiment scores (e.g., daily average polarity) for each asset, with mention volume.
Correlate Sentiment with Financial Data
A correlation matrix and lag analysis showing relationships between sentiment and market movements.
Generate Actionable Insights Report
A finalized report (PDF or dashboard) with visual insights and actionable recommendations for trading or risk management.
Identify the specific financial instruments, sectors, or markets to analyze (e.g., S&P 500, crypto, commodities). Select relevant data sources such as news APIs (e.g., NewsAPI, Bloomberg), social media feeds (e.g., Twitter/X, Reddit r/wallstreetbets), and financial reports (e.g., SEC filings, earnings call transcripts).
Why Brand24: Brand24 provides real-time brand monitoring and competitor sentiment analysis, which directly supports defining sentiment scope and identifying data sources for market sentiment.
Pull unstructured sentiment data from the selected sources using APIs or web scraping. For news, fetch recent headlines and article bodies; for social media, collect posts/comments with timestamps and engagement metrics (likes, retweets). Store raw data in a structured format (e.g., JSON or CSV) with metadata (source, timestamp, asset mentioned).
Why Scrapinghub: Scrapinghub can collect news articles and monitor data from various sources, which aligns with gathering market sentiment data from the web.
Gather quantitative financial data for the same assets and time period to contextualize sentiment. Retrieve price data (open, high, low, close, volume), volatility indices (e.g., VIX), and fundamental metrics (e.g., P/E ratio, earnings surprises). Align timestamps with sentiment data for later correlation analysis.
Why Bloomberg Terminal: Bloomberg Terminal provides real-time market monitoring and quantitative modeling, directly matching the need for financial data collection.
Normalize the raw text data by removing noise (HTML tags, URLs, special characters), lowercasing, and tokenizing. Apply domain-specific preprocessing: expand financial abbreviations (e.g., 'EPS' → 'earnings per share'), remove stop words, and handle emojis/slang (e.g., '🚀' → 'positive'). Split text into sentences for granular analysis.
Why FinGPT: FinGPT specializes in financial sentiment analysis and report summarization, which involves text preprocessing and analysis of financial text data.
Apply a sentiment analysis model tailored to financial language. Use a pre-trained financial BERT model (e.g., FinBERT) or a lexicon-based approach (e.g., Loughran-McDonald dictionary) to assign polarity scores (positive, negative, neutral) to each text entry. Aggregate scores by asset and time period (e.g., daily average sentiment).
Why Hugging Face Spaces: Hugging Face Spaces allows deployment of ML models like FinBERT for sentiment analysis, directly supporting the execution of sentiment analysis.
Merge the sentiment time-series with the financial data (prices, volatility) using date as the key. Calculate correlation coefficients (Pearson or Spearman) between sentiment scores and price returns or volatility. Identify lead-lag relationships (e.g., sentiment precedes price movement by 1 day) using cross-correlation.
Why Thematic: Thematic can analyze unstructured text data and identify key themes, which helps correlate sentiment patterns with financial data.
Compile findings into a concise report with key metrics: top positive/negative assets, sentiment trends, and correlation strengths. Highlight actionable signals (e.g., 'AAPL sentiment dropped 20% before earnings, suggesting caution'). Include visualizations (sentiment vs. price chart, word clouds of top terms) and a summary table. Export as PDF or dashboard.
Why Tableau AI: Tableau AI provides data analysis and visualization capabilities, ideal for generating actionable insights reports from correlated data.
§ Before you start
Teams or solo builders working on finance & legal tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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