## Heatmap: Token Flip Rate by Position Bucket ### Overview The image is a heatmap visualizing the "flip rate" of various tokens (words) across different "position buckets." The heatmap uses a color gradient to represent the flip rate, ranging from dark purple (0.0) to bright yellow (1.0). The tokens are listed on the vertical axis, and the position buckets (0-9) are on the horizontal axis. ### Components/Axes * **Vertical Axis (Token):** Lists individual tokens (words). * Tokens: india, raging, growl, concern, fury, irritate, state, revolting, terrorist, anger, irate, media, offense, threaten, furious, bit, grudge, trump, insult, both, terror, fox, shocking, pout, next, pakistan, terrorism, outrage, hold, boiling, rabid, rage, words, comes, please, hillary, lies, gotta, bitch, also * **Horizontal Axis (Position Bucket):** Represents the position of the token within a sequence or context, divided into 10 buckets (0-9). * **Color Scale (Flip Rate):** Represents the rate at which the token's meaning or sentiment "flips" or changes. * Dark Purple: 0.0 * Light Blue: 0.2 * Green: 0.4 * Yellow-Green: 0.6 * Yellow: 0.8 * Bright Yellow: 1.0 ### Detailed Analysis Here's a breakdown of the flip rate for selected tokens across the position buckets. Note that the values are approximate due to the visual nature of the heatmap. * **india:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 2, 3, 4, 5, 6, 7, 8, and 9. It is low (approximately 0.0) in bucket 1. * **raging:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 3, 4, 5, 6, 7, 8, and 9. It is low (approximately 0.0) in bucket 2. * **growl:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 7, 8, and 9. It is low (approximately 0.0) in bucket 6. * **concern:** Flip rate is high (approximately 0.8-1.0) in buckets 1, 2, 3, 4, 5, 6, 7, 8, and 9. It is low (approximately 0.0) in bucket 0. * **fury:** Flip rate is low (approximately 0.0) in buckets 0, 1, 2, 4, 5, 6, 7, 8, and 9. It is medium (approximately 0.4) in bucket 3. * **irritate:** Flip rate is low (approximately 0.0) in buckets 0, 1, 2, 4, 5, 6, 7, 8, and 9. It is medium (approximately 0.4) in bucket 3. * **state:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **revolting:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **terrorist:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **anger:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **irate:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **media:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **offense:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **threaten:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **furious:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **bit:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **grudge:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **trump:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **insult:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **both:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **terror:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **fox:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **shocking:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **pout:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **next:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **pakistan:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **terrorism:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **outrage:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **hold:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **boiling:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **rabid:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **rage:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **words:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **comes:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **please:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **hillary:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **lies:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **gotta:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **bitch:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. * **also:** Flip rate is high (approximately 0.8-1.0) in buckets 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. ### Key Observations * Some tokens, like "india," "raging," and "growl," show variability in their flip rate across different position buckets. * Other tokens, like "state," "revolting," and "terrorist," consistently have a high flip rate across all position buckets. * The flip rate for "fury" and "irritate" is low across most position buckets, with a slight increase in bucket 3. ### Interpretation The heatmap visualizes how the "flip rate" of different tokens varies depending on their position within a sequence or context. A high flip rate suggests that the token's meaning or sentiment is highly context-dependent and changes frequently. Conversely, a low flip rate indicates that the token's meaning is more stable and consistent across different contexts. The variability in flip rates across different tokens and position buckets suggests that some words are more sensitive to their surrounding context than others. For example, the tokens "india," "raging," and "growl" may have different meanings or connotations depending on where they appear in a sentence or conversation. On the other hand, tokens like "state," "revolting," and "terrorist" may have a more consistent and negative connotation regardless of their position. The slight increase in flip rate for "fury" and "irritate" in bucket 3 could indicate a specific context or pattern where these words are more likely to have a different meaning or sentiment. Further analysis would be needed to understand the specific factors driving these variations in flip rate.

## Heatmap: Token Flip Rate by Position Bucket ### Overview This image presents a heatmap visualizing the "flip rate" of various tokens across different "position buckets". The heatmap uses a color gradient from dark purple (low flip rate) to yellow (high flip rate). The y-axis lists tokens, and the x-axis represents position buckets numbered 0 through 9. ### Components/Axes * **X-axis:** "position bucket" ranging from 0 to 9. * **Y-axis:** "token" listing the following tokens: india, raging, growl, concern, fury, irritate, state, revolting, terrorist, anger, irate, media, offense, threaten, furious, bit, grudge, trump, insult, both, terror, fox, shocking, pout, next, pakistan, terrorism, outrage, hold, boiling, rabid, rage, words, comes, pleas, hillary, lies, gotta, bitch. * **Color Scale (Right):** "flip rate" ranging from 0.0 to 1.0. * **Color Gradient:** Dark purple represents a flip rate near 0.0, transitioning through shades of purple, lavender, and light green, to yellow representing a flip rate near 1.0. ### Detailed Analysis The heatmap displays the flip rate for each token at each position bucket. I will analyze each token's trend across the position buckets and provide approximate flip rate values. * **india:** Flip rate is consistently high (approximately 0.8-1.0) across all position buckets. * **raging:** Starts low (around 0.2 at bucket 0), increases to a peak around 0.8-0.9 at bucket 7, then decreases slightly. * **growl:** Relatively low flip rate (0.2-0.4) for buckets 0-3, then increases to around 0.6-0.8 for buckets 4-9. * **concern:** Low flip rate (0.2-0.4) for buckets 0-2, increases to around 0.6-0.8 for buckets 3-9. * **fury:** Starts around 0.4, increases to 0.8-0.9 at bucket 5, then remains relatively stable. * **irritate:** Low flip rate (0.2-0.4) for buckets 0-4, then increases to around 0.6-0.8 for buckets 5-9. * **state:** Flip rate is consistently high (approximately 0.8-1.0) across all position buckets. * **revolting:** Low flip rate (0.2-0.4) for buckets 0-3, then increases to around 0.6-0.8 for buckets 4-9. * **terrorist:** Flip rate is consistently high (approximately 0.8-1.0) across all position buckets. * **anger:** Starts low (around 0.2 at bucket 0), increases to a peak around 0.8-0.9 at bucket 7, then decreases slightly. * **irate:** Low flip rate (0.2-0.4) for buckets 0-4, then increases to around 0.6-0.8 for buckets 5-9. * **media:** Flip rate is consistently high (approximately 0.8-1.0) across all position buckets. * **offense:** Low flip rate (0.2-0.4) for buckets 0-3, then increases to around 0.6-0.8 for buckets 4-9. * **threaten:** Flip rate is consistently high (approximately 0.8-1.0) across all position buckets. * **furious:** Starts around 0.4, increases to 0.8-0.9 at bucket 5, then remains relatively stable. * **bit:** Low flip rate (0.2-0.4) for buckets 0-2, then increases to around 0.6-0.8 for buckets 3-9. * **grudge:** Low flip rate (0.2-0.4) for buckets 0-3, then increases to around 0.6-0.8 for buckets 4-9. * **trump:** Flip rate is consistently high (approximately 0.8-1.0) across all position buckets. * **insult:** Low flip rate (0.2-0.4) for buckets 0-2, then increases to around 0.6-0.8 for buckets 3-9. * **both:** Flip rate is consistently high (approximately 0.8-1.0) across all position buckets. * **terror:** Flip rate is consistently high (approximately 0.8-1.0) across all position buckets. * **fox:** Low flip rate (0.2-0.4) for buckets 0-3, then increases to around 0.6-0.8 for buckets 4-9. * **shocking:** Low flip rate (0.2-0.4) for buckets 0-2, then increases to around 0.6-0.8 for buckets 3-9. * **pout:** Low flip rate (0.2-0.4) for buckets 0-3, then increases to around 0.6-0.8 for buckets 4-9. * **next:** Flip rate is consistently high (approximately 0.8-1.0) across all position buckets. * **pakistan:** Flip rate is consistently high (approximately 0.8-1.0) across all position buckets. * **terrorism:** Flip rate is consistently high (approximately 0.8-1.0) across all position buckets. * **outrage:** Low flip rate (0.2-0.4) for buckets 0-3, then increases to around 0.6-0.8 for buckets 4-9. * **hold:** Low flip rate (0.2-0.4) for buckets 0-2, then increases to around 0.6-0.8 for buckets 3-9. * **boiling:** Low flip rate (0.2-0.4) for buckets 0-3, then increases to around 0.6-0.8 for buckets 4-9. * **rabid:** Low flip rate (0.2-0.4) for buckets 0-2, then increases to around 0.6-0.8 for buckets 3-9. * **rage:** Starts low (around 0.2 at bucket 0), increases to a peak around 0.8-0.9 at bucket 7, then decreases slightly. * **words:** Low flip rate (0.2-0.4) for buckets 0-3, then increases to around 0.6-0.8 for buckets 4-9. * **comes:** Low flip rate (0.2-0.4) for buckets 0-2, then increases to around 0.6-0.8 for buckets 3-9. * **pleas:** Low flip rate (0.2-0.4) for buckets 0-3, then increases to around 0.6-0.8 for buckets 4-9. * **hillary:** Flip rate is consistently high (approximately 0.8-1.0) across all position buckets. * **lies:** Low flip rate (0.2-0.4) for buckets 0-2, then increases to around 0.6-0.8 for buckets 3-9. * **gotta:** Low flip rate (0.2-0.4) for buckets 0-2, then increases to around 0.6-0.8 for buckets 3-9. * **bitch:** Low flip rate (0.2-0.4) for buckets 0-2, then increases to around 0.6-0.8 for buckets 3-9. ### Key Observations * Tokens like "india", "state", "terrorist", "media", "trump", "both", "terror", "next", "pakistan", "terrorism", and "hillary" consistently exhibit high flip rates across all position buckets. * Many tokens (e.g., "raging", "growl", "concern", "fury", "irritate", "bit", "grudge", "insult", "fox", "shocking", "pout", "outrage", "hold", "boiling", "rabid", "rage", "words", "comes", "pleas", "lies", "gotta", "bitch") show a trend of increasing flip rates as the position bucket number increases. * The flip rate for most tokens is relatively low in the initial position buckets (0-2) and increases towards the higher buckets (7-9). ### Interpretation This heatmap likely represents the probability of a token being "flipped" or altered in some way as it moves through a sequence or process represented by the position buckets. The high flip rates for tokens like "india", "state", "terrorist", "trump", and "hillary" suggest these tokens are consistently subject to change or modification. The increasing flip rates for many other tokens indicate that their likelihood of being altered increases as they progress through the sequence. This could be related to sentiment analysis, where the context or surrounding words influence the interpretation or meaning of the token. The data suggests a dynamic process where certain tokens are inherently more volatile or susceptible to change than others, and the likelihood of change increases over time or position. The consistent high flip rates for politically charged terms suggest a higher degree of manipulation or alteration in their usage.

## [Heatmap]: Token Flip Rate by Position Bucket ### Overview The image is a heatmap (2D grid plot) illustrating the **flip rate** (a measure of state change frequency) for various tokens (words) across 10 position buckets (0–9). The y-axis lists tokens, the x-axis lists position buckets, and a color bar (right) maps colors to flip rates (0.0 = dark purple, 1.0 = yellow; intermediate: teal = 0.4, green = 0.6, light green = 0.8). ### Components/Axes - **Y-axis (Tokens):** 40 tokens (rows) in order: `india, raging, growl, concern, fury, irritate, state, revolting, terrorist, anger, irate, media, offense, threaten, furious, bit, grudge, trump, insult, both, terror, fox, shocking, pout, next, pakistan, terrorism, outrage, hold, boiling, rabid, rage, words, comes, please, hillary, lies, gotta, bitch, also` - **X-axis (Position Buckets):** 10 buckets (columns) labeled `0, 1, 2, 3, 4, 5, 6, 7, 8, 9`. - **Color Bar (Legend):** Vertical bar on the right, labeled `flip rate`, with a gradient from 0.0 (dark purple) to 1.0 (yellow). Intermediate colors: teal (0.4), green (0.6), light green (0.8). ### Detailed Analysis (Key Trends by Token) We analyze flip rates (color) across position buckets (0–9) for each token, focusing on patterns: #### 1. High Flip Rate (Yellow, 1.0) - **Early Positions (0–1):** Tokens like `india, raging, growl, concern, state, media, outrage, rabid, comes, hillary, also` have high rates here. - **Late Positions (3–9):** Tokens like `revolting, irate, threaten, bit, insult, fox, pout, hold, boiling, words, lies, gotta, bitch` have high rates here (low in 0–2). #### 2. Low Flip Rate (Dark Purple, 0.0) - **Early Positions (0–2):** Tokens like `revolting, irate, threaten, bit, insult, fox, pout, hold, boiling, words, lies, gotta, bitch` have low rates here (high in 3–9). - **Late Positions (1–9):** Tokens like `grudge, rabid, comes, hillary, also` have low rates here (high only in 0). #### 3. Medium Flip Rate (Teal = 0.4, Green = 0.6) - **Teal (0.4):** Seen in `irritate (1), terror (2–3, 6–9), shocking (2–3), pakistan (2), terrorism (2, 6–9), please (1)`. - **Green (0.6):** Seen in `fury (2–3), offense (0–1), trump (1–4), terrorism (3), rage (2–3), please (0)`. ### Key Observations - **Position Bucket Patterns:** - Position `0` often has high/medium rates (e.g., `india, raging, growl, concern, state, media, outrage, rabid, comes, hillary, also, offense, trump, terror, please`). - Position `1` is mixed (low for most, medium for `irritate, trump, please`). - Position `2` is mixed (low for most, medium for `fury, terror, shocking, pakistan, terrorism, rage`). - Positions `3–9` are mostly high for many tokens (e.g., `revolting, irate, threaten, bit, insult, fox, pout, hold, boiling, words, lies, gotta, bitch`), with exceptions (e.g., `terrorist, trump, next, pakistan, terrorism, please`). - **Token-Specific Trends:** - `trump`: High at `0`, medium (0.6) at `1–4`, low at `5–9` (more state changes early). - `hillary`: High at `0`, low at `1–9` (fewer changes after the first position). - `terrorist`: Low at `0–2, 5–7`, medium (0.4) at `4`, high at `3, 8–9` (unusual pattern). ### Interpretation This heatmap likely analyzes **token state changes** (e.g., in NLP tasks like sentiment analysis, part-of-speech tagging, or model interpretability) across text positions (e.g., word positions in a sentence/document). - **Position Impact:** Later positions (3–9) often have higher flip rates, suggesting tokens in later text positions are more likely to change state (e.g., a model’s prediction for a word changes as it appears later in a sentence). - **Token Behavior:** Tokens related to anger (`fury, irritate, anger, irate, furious, rage`), terrorism (`terrorist, terror, terrorism`), and politics (`trump, hillary`) show distinct flip patterns, indicating how their “state” (e.g., sentiment, classification) evolves with position. - **Anomalies:** Tokens like `grudge, rabid, comes, hillary, also` have high flip rates only at position `0`, then low—suggesting rare state changes after the first position. This data helps identify how tokens behave across text positions, informing NLP model design or interpretability (e.g., why a model’s prediction for a word changes with its location in a sentence).

# Technical Document: Heatmap Analysis of Token Flip Rates ## 1. Axis Labels and Titles - **Y-Axis (Vertical)**: Labeled "token" with categorical data points representing individual words/phrases. - **X-Axis (Horizontal)**: Labeled "position bucket" with numerical markers from 0 to 9. - **Legend**: Located on the right side of the chart, titled "flip rate" with a color gradient from purple (0.0) to yellow (1.0). ## 2. Categories and Sub-Categories ### Y-Axis Tokens (Categories): - india, raging, growl, concern, fury, irritate, state, revolting, terrorist, anger, irate, media, offense, threaten, furious, bit, grudge, trump, insult, both, terror, fox, shocking, pout, next, pakistan, terrorism, outrage, hold, boiling, rabid, rage, words, comes, please, hillary, lies, gotta, bitch, also. ### X-Axis Position Buckets (Sub-Categories): - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9. ## 3. Data Structure The heatmap represents a **10xN matrix** where: - **Rows**: Tokens (Y-axis categories). - **Columns**: Position buckets (X-axis sub-categories). - **Cells**: Color intensity indicating flip rate (0.0–1.0). ## 4. Key Trends and Observations ### Spatial Grounding of Legend - The legend is positioned **vertically on the right side** of the chart, spanning the full height of the plot. Its color gradient aligns with the heatmap's color scale. ### Trend Verification - **High Flip Rates (Yellow)**: - "india" shows a strong yellow bar in position bucket 0. - "rage" exhibits high flip rates in position buckets 2 and 5. - "trump" has a yellow bar in position bucket 4. - **Low Flip Rates (Purple)**: - "bitch" has a dark purple bar in position bucket 8. - "also" shows minimal flip rates across most buckets. - **Moderate Flip Rates (Green/Blue)**: - "media" and "terrorism" display green bars in position buckets 3 and 7, respectively. - "both" and "shocking" have blue bars in position buckets 2 and 5. ### Component Isolation - **Header**: Axis titles ("token" and "position bucket"). - **Main Chart**: Grid of colored cells representing flip rates. - **Legend**: Color scale (0.0–1.0) with no numerical annotations. ## 5. Data Table Reconstruction | Token | Position Bucket 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |-------------|-------------------|---|---|---|---|---|---|---|---|---| | india | Yellow | ... | ... | ... | ... | ... | ... | ... | ... | ... | | raging | ... | Yellow | ... | ... | ... | ... | ... | ... | ... | ... | | growl | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | | bitch | ... | ... | ... | ... | ... | ... | ... | ... | Purple | ... | | also | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | *Note: "..." indicates unspecified flip rates (color intensity varies).* ## 6. Color-Legend Cross-Reference - **Purple (0.0)**: Minimal flip rate (e.g., "bitch" in position 8). - **Yellow (1.0)**: Maximum flip rate (e.g., "india" in position 0). - **Green/Blue (0.2–0.8)**: Intermediate flip rates (e.g., "media" in position 3). ## 7. Conclusion The heatmap visualizes token-specific flip rates across 10 position buckets. High flip rates (yellow) are concentrated in specific tokens and positions, while low rates (purple) indicate infrequent flips. No numerical data is explicitly provided; trends are inferred from color intensity.