Aspect-Based Sentiment Analysis Advanced

from transformers import pipeline

# Zero-shot classification can be used for ABSA
classifier = pipeline("zero-shot-classification")

review = "The food was amazing but the service was incredibly slow."
aspects = ["food quality", "service speed", "ambiance", "price"]

result = classifier(review, aspects, multi_label=True)
for label, score in zip(result["labels"], result["scores"]):
    print(f"{label}: {score:.3f}")

from openai import OpenAI
import json

client = OpenAI()

review = """The laptop has a stunning display and the keyboard is
comfortable for long typing sessions. However, the fan noise
is unbearable and the battery barely lasts 3 hours."""

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{
        "role": "user",
        "content": f"""Analyze the following review and extract aspects
with their sentiment (positive/negative/neutral) and a brief reason.
Return as JSON array.

Review: {review}"""
    }],
    response_format={"type": "json_object"}
)

aspects = json.loads(response.choices[0].message.content)
print(json.dumps(aspects, indent=2))
# {
#   "aspects": [
#     {"aspect": "display", "sentiment": "positive", "reason": "described as stunning"},
#     {"aspect": "keyboard", "sentiment": "positive", "reason": "comfortable for long use"},
#     {"aspect": "fan noise", "sentiment": "negative", "reason": "described as unbearable"},
#     {"aspect": "battery", "sentiment": "negative", "reason": "barely lasts 3 hours"}
#   ]
# }

import spacy
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

nlp = spacy.load("en_core_web_sm")
analyzer = SentimentIntensityAnalyzer()

# Define aspects to track
aspect_keywords = {
    "food": ["food", "meal", "dish", "taste", "flavor"],
    "service": ["service", "staff", "waiter", "server"],
    "price": ["price", "cost", "expensive", "cheap", "value"],
    "ambiance": ["ambiance", "atmosphere", "decor", "music"],
}

def analyze_aspects(review):
    doc = nlp(review)
    results = {}
    for sent in doc.sents:
        sent_text = sent.text.lower()
        for aspect, keywords in aspect_keywords.items():
            if any(kw in sent_text for kw in keywords):
                score = analyzer.polarity_scores(sent.text)["compound"]
                results[aspect] = score
    return results

review = "The food was delicious and well presented. The service was slow but friendly. Prices are a bit high for the portion size."
print(analyze_aspects(review))
# {'food': 0.65, 'service': 0.05, 'price': -0.34}

import pandas as pd

# Analyze all reviews
all_aspects = []
for review in reviews:
    aspects = analyze_aspects(review)
    all_aspects.append(aspects)

# Create summary DataFrame
aspect_df = pd.DataFrame(all_aspects)
summary = aspect_df.describe().loc[["count", "mean"]]
print(summary)
# Shows average sentiment per aspect across all reviews