Market Sentiment Analyzer

1. Overview

The Market Sentiment Analyzer reviews a set of recent news articles, social‑media posts, and research reports, determines the overall positive, negative, or neutral sentiment for each item, aggregates the results, and produces a concise report that highlights key investment insights.

2. Business Value

• Provides the Portfolio Manager with a quick, data‑driven snapshot of market sentiment across multiple channels.

• Helps identify emerging opportunities or threats before they are reflected in price movements.

• Enables faster, evidence‑based decision making for portfolio allocation and risk management.

3. Operational Context

• When to run: When a portfolio manager needs a timely, cross‑source view of market sentiment (e.g., before a market‑open meeting, after a major event, or on a regular weekly review).

• Who uses it: Portfolio Managers, Research Analysts, and senior investment decision‑makers.

• Frequency: Typically daily or weekly, depending on the market’s activity level and the portfolio’s trading cadence.

4. Inputs

Required Inputs

Derived Inputs (Generated by Agent)

Note: All derived inputs are automatically generated by the agent. The process can still run if any of the three content sources (News, Social, Research) are missing; the missing source will be noted as “No … data provided”.

5. Outputs

Sentiment Summary Report

A six section report:

1. Executive Summary (overall market sentiment).

2. Source‑by‑Source Sentiment (breakdown for news, social, research).

3. Key Positive/Negative Highlights (top 3 items each with brief excerpt).

4. Overall Sentiment Score (numeric scale ‑10 to +10, where negative values indicate bearish sentiment, positive indicates bullish sentiment).

5. Investment Insight (actionable recommendation).

   • Use headings and bullet points for readability.

   • All numeric values displayed with one decimal place.

   • Use neutral and professional tone.

6. Raw Sentiment Scores

   • List of individual scores for every input item (title, source, sentiment classification, sentiment score).

   • Tabular format, columns:

     • Title/Content, Source, Sentiment (Positive/Negative/Neutral), Score (‑10 to +10)

6. Detailed Plan & Execution Steps

1. Gather Required Input

   • Receive Market Description (and any optional Proprietary Research PDFs) from the user.

2. Initialize Process Metadata

   • Set Short Name, Description, Industry, and Persona from SOP metadata (derived inputs).

3. Research & Retrieve Content Sources

   • 3.1 News Articles – Query news APIs for finance‑related articles published recently. Extract required fields; if none, flag “No news data provided”.

   • 3.2 Social Media Posts – Query Twitter and Reddit for finance‑related postsrecently. Extract required fields; if none, flag “No social‑media data provided”.

   • 3.3 Research Reports – Search public repositories for relevant, text‑based PDFs released recently. Append any Proprietary Research PDFs supplied. If none, flag “No research data provided”.

4. Verify Completeness

   • Confirm each derived source list contains at least one entry. If a list is empty, record the appropriate “No … data provided” note and continue with the remaining sources.

5. Extract Text

   • For each news article, pull Title, Source, Published Date, and Excerpt.

   • For each social post, pull Platform, User Handle, Posted Date/Time, and Content.

   • For each research PDF, extract the full textual content (ignore images, tables, footnotes).

6. Clean Text

   • Remove HTML tags, URLs, and non‑alphabetic symbols.

   • Convert all text to sentence case for consistent processing.

7. Perform Sentiment Classification

   • Apply the simple rule‑based classifier using the Sentiment Keyword Set (Appendix C).

   • Positive: presence of any positive keyword.

   • Negative: presence of any negative keyword.

   • Neutral: no clear positive or negative cues.

   • Score = (positive‑keyword‑count – negative‑keyword‑count) × 2.5, capped at –10 and +10. If net count = 0 → Neutral, score = 0.0.

8. Aggregate Scores by Source

   • Compute the average score for each source type (news, social, research).

   • Compute the Overall Sentiment Score as the simple average of the three source averages (equal weight).

9. Identify Highlights

   • From each source, select the three items with the highest positive scores and the three items with the lowest (most negative) scores.

   • Capture Title/User/Report Title and a short excerpt (≤ 100 characters).

10. Draft the Sentiment Summary Report

    • Executive Summary – Present the Overall Sentiment Score and a brief interpretation (e.g., “Overall sentiment is mildly bullish”).

    • Source‑by‑Source Sentiment – List each source’s average score with a concise comment.

    • Key Positive Highlights – Bullet list of top positive items (title + excerpt).

    • Key Negative Highlights – Bullet list of top negative items (title + excerpt).

    • Investment Insight – A concise recommendation (e.g., “Consider increasing exposure to technology stocks; monitor for potential sell‑off in energy sector”).

11. Generate Raw Sentiment Scores Table

    • Create a table with columns: Title/Content, Source, Sentiment, Score.

12. Review & Finalize

    • Proofread for spelling, grammar, and correct attribution.

    • Verify that all required sections are present and formatting follows the rules.

13. Deliver Output

    • Provide the Sentiment Summary Report in plain‑text format.

    • Provide the Raw Sentiment Scores table.

7. Validation & Quality Checks

• Missing Data Check: Ensure every required field (title, source, date, content) is present for each automatically retrieved item; if missing, flag the item for manual review and omit it from scoring.

• Derived Input Check: Confirm that each derived source (News, Social, Research) contains the fields listed in the “Extract Text” step.

• Score Range Check: Confirm every computed score falls within the –10 to +10 range; any out‑of‑range value triggers a review of the keyword counts.

• Aggregation Accuracy: Verify the average scores are computed correctly (total scores ÷ number of items).

• Duplicate Detection: Ensure no duplicate items appear in the same list; duplicates are removed before analysis.

• Report Consistency: Check that the Overall Sentiment Score matches the weighted average of the three source averages.

8. Special Rules / Edge Cases

• Empty Source List: If a source list (e.g., Social Media Posts) is empty, add a note “No social‑media data provided” and still calculate the Overall Score using the other sources.

• Identical Positive/Negative Scores: If multiple items tie for the top/bottom three, select the ones with the most recent date.

• Neutral‑Only Output: If all items are classified as neutral, the Summary should state “Neutral market sentiment across all sources.”

• Missing Keywords: If a text contains no recognized keywords, assign a Neutral sentiment and a score of 0.0.

• Error Handling: If a required field is missing from any item, stop the process, generate an error message identifying the missing field, and do not produce any output.

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Appendix A – FAQ

Q1: What if a news article is behind a paywall? A1: The agent extracts only the excerpt that is publicly accessible. If the excerpt is < 50 characters, the item is flagged “insufficient text for analysis” and excluded from scoring.

Q2: Can the process handle non‑English content? A2: This SOP expects English text. Non‑English content is flagged as “unsupported language” and omitted.

Q3: How are emojis handled in social posts? A3: Emojis are ignored; only the textual content is analyzed.

Q4: What if a research PDF is scanned image? A4: The system cannot extract text from scanned images. The PDF is marked “unreadable”, and the research source is noted as “No research data provided”.

Q5: What is the meaning of the Overall Sentiment Score range? A5: –10 = extremely bearish; +10 = extremely bullish; 0 = neutral. The magnitude reflects the strength of the sentiment.

Q6: How are ties for top or bottom items resolved? A6: The most recent items (by date) are prioritized.

Q7: Can the SOP be used for other industries? A8: The steps remain the same; adjust the keyword lists in the sentiment rules to match industry‑specific language.

Q8: What if an item contains both positive and negative keywords? A9: Count both sets; the net score determines the classification (e.g., more positive words → Positive).

Q9: How often should the analysis be performed? A10: Typically daily or weekly, depending on market volatility and the portfolio’s turnover frequency.

Q10: What if a required field is missing from an input item? A11: The process stops for that run, outputs a clear error message indicating the missing field, and does not produce any output.

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Appendix B – Glossary

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Appendix C – Reference Materials

A. Sentiment Keywords

Rules:

• Count each occurrence of a positive or negative keyword.

• Score = (positive count – negative count) × 2.5, capped at –10 and +10.

• If net count = 0 → Neutral sentiment, score = 0.0.

B. Report Formatting Guide

• Headers: Title case (e.g., “Executive Summary”).

• Bullet Points: Dash “-” for each bullet.

• Numbers: Use numeric digits; keep one decimal place for scores (e.g., “+3.4”).

• Tone: Formal and professional, no slang.

• Length: Report should not exceed 500 words for concise reading.

C. Example Working Report (Sample)

Executive Summary Overall sentiment is +4.2 – strongly bullish.

Source‑by‑Source Sentiment

• News: Avg. +5.1 – Tech and earnings drive optimism.

• Social: Avg. +3.7 – Positive chatter on AI.

• Research: +4.5 – Growth expectations high.

Key Positive Highlights

• “Tech Stocks Rally on Strong Earnings” – +6.0 – 5% surge on earnings beat.

• “Just bought more AI stocks—look at the upside!” – +5.0 – bullish AI sentiment.

• “Q3 2025 Economic Outlook” – +8.0 – 3.2% growth projection.

Key Negative Highlights

• “Oil Price Slip” – –2.5 – 2% decline due to supply relief.

• “Not convinced the Fed…” – –3.0 – caution on rate cuts.

Investment Insight Increase allocation to technology and AI. Review oil exposure; consider hedging if further declines are expected.