Seeking Authentic Recognition: A Soul's Yearning for Deep Connection

With every beat of my heart, there is a story. My life is a complex story, yet its true depth often remains hidden in everyday interactions. Days become weeks, months into years, forming a tapestry of experiences. Today, I won’t blame the world for not fully understanding. After all, behind the everyday facade lies a personal narrative that words often fail to capture.

The Echo of ‘Iris’

Sometimes, I hear echoes of ’ Iris.’ The haunting melody of the Goo Goo Dolls resonates deeply, stirring emotions buried within. The lyrics touch hidden parts of myself, giving voice to a profound longing—a yearning for the comfort of a gaze that truly perceives.

The chorus rings out—“I just want you to know who I am.”

Beyond Validation: The Need to Be Seen

The song isn’t just about ‘exposing vulnerabilities’; it’s about the desire to share them, to have them held with care and understood in their entirety. I crave that connection—a resonance between souls, to be deeply seen. Not just for the outward displays of laughter and courage, but for the hidden fears, the lost innocence, and the persistent hopes despite life’s disappointments.

Why this desire? It transcends mere validation. It’s a yearning for acknowledgment that ‘these textures of my soul are real’, that they exist beyond my own perception.

Unmasking the Inner World

I am more than my surface persona—more than the typical 32-year-old, neatly dressed, observing life with quiet contemplation, casually dismissing profound questions about love and life.

By looking beyond the surface, unspoken narratives emerge: the comfort of childhood lullabies, the joy of the first summer rain, and the quiet sorrows that have shaped me. A strong inner drive hides behind a calm exterior, with dreams taking shape during sleepless nights, fueled by a passion that resists harsh realities.

The Hope for Shared Recognition

Meeting me is like encountering a book whose words are a mask. Behind each expression lie untold chapters. I wish someone would be willing to read it, turning the pages with care, understanding the ‘abstract metaphors’, and deciphering the hidden meanings.

Every moment of eye contact feels like an opportunity for connection. I seek that mirrored recognition, an echo of my inner world in another’s gaze. It’s not simply about being noticed; it’s about someone looking at me and saying, “I see you, and I see myself. We understand each other.”

Until I find that recognition, until those words are etched in someone’s eyes, I will continue painting my emotions on the ‘canvas of solitude’, carving my dreams in silence, longing for the day someone breaks through the quiet of my heart—wanting to truly understand, desiring to meet my eyes and see me, unmasked and free.

As ‘Iris’ echoes in the stillness, I hold onto the hope that there’s a melody strong enough, a vision clear enough, a love brave enough, to discern the ‘true narrative of my life’.

import pandas as pd
import random

# Create a sample dataset
data = {
    'Narrative': [
        "I love listening to music.",
        "Music helps me express my emotions.",
        "I have endured a lot of pain in my life.",
        "I admire bravery in people.",
        "Persistence is the key to success."
    ],
    'Love_for_Music': [random.randint(1, 5) for _ in range(5)],
    'Depth_of_Emotions': [random.randint(1, 5) for _ in range(5)],
    'Pain_Endured': [random.randint(1, 5) for _ in range(5)],
    'Bravery': [random.randint(1, 5) for _ in range(5)],
    'Persistence': [random.randint(1, 5) for _ in range(5)],
    'Understood': [random.choice([0, 1]) for _ in range(5)]
}

# Create a DataFrame
sample_df = pd.DataFrame(data)

# Save the DataFrame to a CSV file
sample_df.to_csv('people.csv', index=False)

!pip install transformers

import nltk
nltk.download('vader_lexicon')

from transformers import RobertaModel, RobertaTokenizer
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score
from scipy import spatial
import pandas as pd
import torch

tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaModel.from_pretrained('roberta-base')
sia = SentimentIntensityAnalyzer()

def get_roberta_embeddings(text):
    tokens = tokenizer.tokenize(text)
    encoded_input = tokenizer.convert_tokens_to_ids(tokens)
    attention_mask = torch.tensor([1]*len(encoded_input))

    with torch.no_grad():
        last_hidden_state = model(torch.tensor([encoded_input]), attention_mask = torch.tensor([attention_mask]))
    return last_hidden_state[0].numpy().mean(axis=1)

df = pd.read_csv('people.csv')
narratives = df['Narrative'].values
embeddings = []

for narrative in narratives:
    embeddings.append(get_roberta_embeddings(narrative))

df['Sentiment'] = df['Narrative'].apply(lambda narrative: sia.polarity_scores(narrative)['compound'])
df_emb = pd.DataFrame(embeddings)
df_features = pd.concat([df[['Love_for_Music', 'Depth_of_Emotions', 'Pain_Endured', 'Bravery', 'Persistence']], df_emb, df['Sentiment']], axis=1)

Y = df['Understood']
X_train, X_test, Y_train, Y_test = train_test_split(df_features, Y, test_size = 0.2, random_state = 42)

model = RandomForestClassifier()
model.fit(X_train, Y_train)
Y_pred = model.predict(X_test)

cm = confusion_matrix(Y_test, Y_pred)
accuracy = accuracy_score(Y_test, Y_pred)

print('Confusion Matrix:')
print(cm)
print(f'Accuracy: {accuracy * 100}%')