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
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Provides the Portfolio Manager with a quick, data‑driven snapshot of market sentiment across multiple channels.
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Helps identify emerging opportunities or threats before they are reflected in price movements.
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Enables faster, evidence‑based decision making for portfolio allocation and risk management.
3. Operational Context
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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).
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Who uses it: Portfolio Managers, Research Analysts, and senior investment decision‑makers.
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Frequency: Typically daily or weekly, depending on the market’s activity level and the portfolio’s trading cadence.
4. Inputs
Required Inputs
| Name/Label | Type | Details Provided |
|---|
| Market Description | Text | A brief description of the market you'd like to analyze (e.g. a segment, field, geography) |
| Proprietary Research PDFs (optional) | List of PDF files | Any non‑public research documents the user wishes to include. If none are supplied, the agent will rely solely on publicly available research. |
Derived Inputs (Generated by Agent)
| Name/Label | Type | How Obtained |
|---|
| Short Name | Text | Hard‑coded from SOP metadata: “Market Sentiment Analyzer”. |
| Description | Text | Hard‑coded from SOP metadata: “Analyze news, social, and research sentiment for investment insights”. |
| Industry | Text | Hard‑coded from SOP metadata: “Financial Services / Fintech”. |
| Persona | Text | Hard‑coded from SOP metadata: “Portfolio Manager”. |
| News Articles | List of items | • Query news aggregators (e.g., Bloomberg, Reuters, Google News) for recent articles published that contain finance‑related keywords (e.g., “market”, “stock”, “investment”). |
| • For each article, extract Title, Source, Published Date, and an Excerpt (first ≤ 300 characters). | | |
| • If no articles are found, record “No news data provided”. | | |
| Social Media Posts | List of items | • Call public social‑media APIs (Twitter, Reddit) for posts on the that contain finance‑related keywords. |
| • Extract Platform, User Handle, Posted Date/Time, and Content (full text, respecting platform limits). | | |
| • If none are found, record “No social‑media data provided”. | | |
| Research Reports | List of documents | • Search public repositories (SEC EDGAR, analyst portals, think‑tank sites) for text‑based PDFs released recently that are relevant to the Industry. |
| • Extract PDF Title and full text content (ignore images/tables). | | |
| • Append any Proprietary Research PDFs supplied by the user. | | |
| • If no reports are found, record “No research data provided”. | | |
| Sentiment Keyword Set | List of strings | Compile the positive and negative keyword lists from Appendix C – Sentiment Keywords, filtered for relevance to the Financial Services / Fintech industry. This set drives the classification rules. |
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:
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Executive Summary (overall market sentiment).
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Source‑by‑Source Sentiment (breakdown for news, social, research).
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Key Positive/Negative Highlights (top 3 items each with brief excerpt).
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Overall Sentiment Score (numeric scale ‑10 to +10, where negative values indicate bearish sentiment, positive indicates bullish sentiment).
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Investment Insight (actionable recommendation).
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Use headings and bullet points for readability.
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All numeric values displayed with one decimal place.
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Use neutral and professional tone.
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Raw Sentiment Scores
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List of individual scores for every input item (title, source, sentiment classification, sentiment score).
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Tabular format, columns:
- Title/Content, Source, Sentiment (Positive/Negative/Neutral), Score (‑10 to +10)
6. Detailed Plan & Execution Steps
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Gather Required Input
- Receive Market Description (and any optional Proprietary Research PDFs) from the user.
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Initialize Process Metadata
- Set Short Name, Description, Industry, and Persona from SOP metadata (derived inputs).
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Research & Retrieve Content Sources
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3.1 News Articles – Query news APIs for finance‑related articles published recently. Extract required fields; if none, flag “No news data provided”.
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3.2 Social Media Posts – Query Twitter and Reddit for finance‑related postsrecently. Extract required fields; if none, flag “No social‑media data provided”.
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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”.
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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.
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Extract Text
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For each news article, pull Title, Source, Published Date, and Excerpt.
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For each social post, pull Platform, User Handle, Posted Date/Time, and Content.
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For each research PDF, extract the full textual content (ignore images, tables, footnotes).
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Clean Text
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Remove HTML tags, URLs, and non‑alphabetic symbols.
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Convert all text to sentence case for consistent processing.
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Perform Sentiment Classification
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Apply the simple rule‑based classifier using the Sentiment Keyword Set (Appendix C).
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Positive: presence of any positive keyword.
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Negative: presence of any negative keyword.
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Neutral: no clear positive or negative cues.
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Score = (positive‑keyword‑count – negative‑keyword‑count) × 2.5, capped at –10 and +10. If net count = 0 → Neutral, score = 0.0.
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Aggregate Scores by Source
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Compute the average score for each source type (news, social, research).
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Compute the Overall Sentiment Score as the simple average of the three source averages (equal weight).
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Identify Highlights
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From each source, select the three items with the highest positive scores and the three items with the lowest (most negative) scores.
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Capture Title/User/Report Title and a short excerpt (≤ 100 characters).
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Draft the Sentiment Summary Report
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Executive Summary – Present the Overall Sentiment Score and a brief interpretation (e.g., “Overall sentiment is mildly bullish”).
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Source‑by‑Source Sentiment – List each source’s average score with a concise comment.
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Key Positive Highlights – Bullet list of top positive items (title + excerpt).
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Key Negative Highlights – Bullet list of top negative items (title + excerpt).
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Investment Insight – A concise recommendation (e.g., “Consider increasing exposure to technology stocks; monitor for potential sell‑off in energy sector”).
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Generate Raw Sentiment Scores Table
- Create a table with columns: Title/Content, Source, Sentiment, Score.
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Review & Finalize
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Proofread for spelling, grammar, and correct attribution.
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Verify that all required sections are present and formatting follows the rules.
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Deliver Output
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Provide the Sentiment Summary Report in plain‑text format.
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Provide the Raw Sentiment Scores table.
7. Validation & Quality Checks
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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.
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Derived Input Check: Confirm that each derived source (News, Social, Research) contains the fields listed in the “Extract Text” step.
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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.
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Aggregation Accuracy: Verify the average scores are computed correctly (total scores ÷ number of items).
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Duplicate Detection: Ensure no duplicate items appear in the same list; duplicates are removed before analysis.
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Report Consistency: Check that the Overall Sentiment Score matches the weighted average of the three source averages.
8. Special Rules / Edge Cases
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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.
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Identical Positive/Negative Scores: If multiple items tie for the top/bottom three, select the ones with the most recent date.
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Neutral‑Only Output: If all items are classified as neutral, the Summary should state “Neutral market sentiment across all sources.”
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Missing Keywords: If a text contains no recognized keywords, assign a Neutral sentiment and a score of 0.0.
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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.
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.
Appendix B – Glossary
| Term | Definition |
|---|
| Sentiment | The emotional tone (positive, negative, neutral) expressed in a piece of text. |
| Overall Sentiment Score | Weighted average of the three source averages (news, social, research) on a –10 to +10 scale. |
| Positive Sentiment | Language indicating optimism, growth, or favorable outlook. |
| Negative Sentiment | Language indicating decline, risk, or unfavorable outlook. |
| Neutral Sentiment | No clear positive or negative language; balanced or factual. |
| Research Report | A PDF document containing analyst or research‑firm analysis, typically in PDF format. |
| Excerpt | Short excerpt (up to 300 characters) from a news article, or up to 100 characters from a social post, used for summarizing. |
| Keyword | Word used in the simple sentiment algorithm (e.g., “gain”, “loss”, “upgrade”). |
| Portfolio Manager | Individual responsible for allocating and managing investment assets. |
| Investment Insight | A concise recommendation derived from the sentiment analysis. |
Appendix C – Reference Materials
A. Sentiment Keywords
| Sentiment Category | Positive Keywords | Negative Keywords |
|---|
| Positive | gain, growth, surge, rally, upside, outperformance, upgrade, strong, bullish, beat estimates, record high, solid, robust, profit, revenue increase, positive outlook, outperform, buy, increase, rise, outperforming, optimism | |
| Negative | loss, decline, fall, drop, downside, downgrade, underperform, weakness, bearish, hit, slump, deficit, risk, concern, warning, uncertainty, negative outlook, sell, decrease, low, slowdown, warning | |
Rules:
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Count each occurrence of a positive or negative keyword.
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Score = (positive count – negative count) × 2.5, capped at –10 and +10.
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If net count = 0 → Neutral sentiment, score = 0.0.
B. Report Formatting Guide
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Headers: Title case (e.g., “Executive Summary”).
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Bullet Points: Dash “-” for each bullet.
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Numbers: Use numeric digits; keep one decimal place for scores (e.g., “+3.4”).
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Tone: Formal and professional, no slang.
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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
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News: Avg. +5.1 – Tech and earnings drive optimism.
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Social: Avg. +3.7 – Positive chatter on AI.
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Research: +4.5 – Growth expectations high.
Key Positive Highlights
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“Tech Stocks Rally on Strong Earnings” – +6.0 – 5% surge on earnings beat.
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“Just bought more AI stocks—look at the upside!” – +5.0 – bullish AI sentiment.
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“Q3 2025 Economic Outlook” – +8.0 – 3.2% growth projection.
Key Negative Highlights
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“Oil Price Slip” – –2.5 – 2% decline due to supply relief.
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“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.
