Introduction

My curiosity about sentiment analysis led me to experiment with open-source datasets. Rather than send sensitive user text to cloud APIs, it might be better to use a local classification pipeline that labels sentiment and categorizes data in real time.

Why Local Classification?

  • User Privacy: Customer feedback stays on-prem.
  • Customization: I can tweak labels (e.g., “bug report”, “feature request”).
  • Speed & Cost: Instant predictions without per-request charges.

Pipeline Overview

  1. Choose or Fine-Tune Model – Pick a classification-capable LLM or fine-tune with LoRA.
  2. Data Preparation – Collect and clean labeled examples (CSV, JSONL).
  3. Inference Pipeline – Wrap model in a predict function for batch or streaming.
  4. Visualization & Alerts – Hook into dashboards or trigger alerts on negative spikes.

I’ll detail each step with code snippets and my own notes on best practices.

1. Choosing & Fine-Tuning the Model

For quick experiments, I use distilbert-base-uncased-finetuned-sst-2-english from HF. If I need custom labels, I fine-tune via LoRA.

pip install transformers peft datasets

from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
from peft import LoraConfig, get_peft_model
from datasets import load_dataset

# Base model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
model     = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)

# Optional: LoRA setup for custom labels
peft_config = LoraConfig(task_type='SEQ_CLS', r=8, alpha=16, dropout=0.1)
model = get_peft_model(model, peft_config)

# Load custom labeled dataset
ds = load_dataset('csv', data_files='labeled_data.csv')
enc = tokenizer(ds['text'], truncation=True, padding=True)
ts = ds.map(lambda x: tokenizer(x['text'], truncation=True, padding=True), batched=True)

# Training arguments
args = TrainingArguments(
    output_dir='sentiment-model',
    per_device_train_batch_size=8,
    num_train_epochs=2,
    logging_steps=50
)

trainer = Trainer(
    model=model,
    args=args,
    train_dataset=ts
)
trainer.train()

model.save_pretrained('custom-sentiment-model')

Once I have a custom model, it feels more tuned to my domain (e.g., SaaS feedback vs. movie reviews).

2. Data Preparation

Why: Garbage in, garbage out. I gather open source data, and balance labels.

import pandas as pd

df = pd.read_csv('raw_feedback.csv')
# Keep only text column and label
df = df[['comment', 'sentiment']]
# Drop nulls and duplicates
df.dropna(inplace=True)
df.drop_duplicates(subset=['comment'], inplace=True)

# Inspect label distribution
dist = df['sentiment'].value_counts()
print(dist)

# Save cleaned data
df.to_csv('labeled_data.csv', index=False)

I look at the label distribution to ensure I have enough positive and negative samples—if not, I manually label more or use simple heuristics to bootstrap.

3. Inference Pipeline

Why: I want a reusable function I can call in scripts, webhooks, or batch jobs.

from transformers import pipeline

classifier = pipeline(
    'text-classification',
    model='custom-sentiment-model',
    tokenizer=tokenizer,
    device=0
)

def predict_sentiment(texts):
    return classifier(texts)

# Example batch
samples = ["Love the new UI!", "This crashed my app."]
results = predict_sentiment(samples)
print(results)

If I notice misclassifications, I add those examples back into my training set for future fine-tuning.

4. Visualization & Alerts

Why: I need to spot when negative sentiment spikes so I can investigate.

  • Dashboard: I push results to a simple Plotly Dash app or Grafana via Prometheus metrics.
  • Alerts: If >10% of last 100 comments are negative, trigger a Slack alert via webhook.
import requests

def check_and_alert(results):
    neg_count = sum(1 for r in results if r['label']=='NEGATIVE')
    if neg_count / len(results) > 0.1:
        requests.post(SLACK_WEBHOOK_URL, json={
            'text': f"High negative feedback: {neg_count}/{len(results)} in last batch"
        })

This automation could possibly catch sudden changes in sentiment amongst users.

Wrapping Up

With this sentiment and classification pipeline, it is possible to process feedback in real time, customize labels, and integrate alerts—all locally. Next, I’ll explore vision-language search—combining image embeddings with text queries.