Merge pull request #14 from elsong86/vader-feature

Swapped out textblob for spaCY and Vader for added functionalities

backend/app/routers/outscraper_reviews.py

@@ -1,15 +1,16 @@
 from fastapi import APIRouter, HTTPException, Query, Depends, Request
 from outscraper import ApiClient
-from textblob import TextBlob
 import logging
 import json
 import os
 from datetime import datetime, timezone, timedelta
 from ..utils.rate_limiter import rate_limiter
 from ..utils.redis_utils import redis_client
 from ..services.supabase_service import SupabaseService
+from ..utils.sentiment_analysis import analyze_sentiments
+from lingua import Language, LanguageDetectorBuilder
 from pydantic import BaseModel, Field
-from typing import Callable  
+from typing import Callable
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -21,6 +22,19 @@
 # Instantiate the SupabaseService using the modularized Supabase client
 supabase_service = SupabaseService()
 
+# Initialize Lingua language detector with English enabled
+detector = LanguageDetectorBuilder.from_languages(Language.ENGLISH, Language.SPANISH).build()
+
+# Define the is_english function
+def is_english(text):
+    try:
+        # Detect language using Lingua
+        detected_language = detector.detect_language_of(text)
+        return detected_language == Language.ENGLISH
+    except Exception as e:
+        logger.error(f"Error detecting language: {e}")
+        return False
+
 router = APIRouter()
 
 def check_cache(place_id: str):
@@ -42,7 +56,11 @@ def fetch_reviews_from_api(place_id: str):
         )
         if results and isinstance(results, list) and len(results) > 0:
             reviews = results[0].get('reviews_data', [])
-            non_empty_reviews = [review for review in reviews if review.get('review_text') and review['review_text'].strip()]
+
+            # Filter out non-English reviews using is_english
+            english_reviews = [review for review in reviews if is_english(review['review_text'])]
+
+            non_empty_reviews = [review for review in english_reviews if review.get('review_text') and review['review_text'].strip()]
 
             # Cache the fetched reviews
             cache_key = f"reviews:{place_id}"
@@ -55,6 +73,7 @@ def fetch_reviews_from_api(place_id: str):
         logger.error(f"Error fetching reviews from API: {str(e)}")
         raise HTTPException(status_code=500, detail=str(e))
 
+
 def store_restaurant(place_id: str, name: str, address: str):
     try:
         # Check if the restaurant already exists
@@ -81,15 +100,6 @@ def store_restaurant(place_id: str, name: str, address: str):
         logger.error(f"Failed to store restaurant: {str(e)}")
         raise HTTPException(status_code=500, detail=str(e))
 
-
-def analyze_sentiments(reviews):
-    sentiments = [TextBlob(review['review_text']).sentiment.polarity for review in reviews]
-    if sentiments:
-        average_sentiment = sum(sentiments) / len(sentiments)
-    else:
-        average_sentiment = 0.0
-    return average_sentiment
-
 def get_stored_reviews(place_id: str):
     # Define your freshness criteria (e.g., 1 week)
     freshness_limit = timedelta(weeks=1)

backend/app/utils/sentiment_analysis.py

@@ -0,0 +1,44 @@
+import spacy
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Initialize spaCy and VADER
+nlp = spacy.load("en_core_web_sm")
+analyzer = SentimentIntensityAnalyzer()
+
+def clean_text(text):
+    doc = nlp(text)
+    cleaned_tokens = []
+
+    for token in doc:
+        # Only keep alphabetic tokens, remove stopwords and punctuation
+        if token.is_alpha and not token.is_stop:
+            cleaned_tokens.append(token.text.lower())
+
+    return ' '.join(cleaned_tokens)
+
+def analyze_sentiments(reviews):
+    sentiments = []
+
+    for review in reviews:
+        review_text = review['review_text']
+
+        # Clean the review text using spaCy
+        cleaned_text = clean_text(review_text)
+
+        # Perform sentiment analysis with VADER on the cleaned text
+        sentiment_score = analyzer.polarity_scores(cleaned_text)['compound']
+
+        # Rescale VADER score from [-1, 1] to [0, 10]
+        scaled_score = (sentiment_score + 1) * 5  # Transforms the score to [0, 10]
+        sentiments.append(scaled_score)
+
+    # Calculate the average sentiment
+    if sentiments:
+        average_sentiment = sum(sentiments) / len(sentiments)
+    else:
+        average_sentiment = 0.0
+
+    return average_sentiment

backend/requirements.txt

@@ -5,16 +5,22 @@ annotated-types==0.7.0
 anyio==4.4.0
 attrs==24.2.0
 Authlib==1.3.2
+blis==1.0.1
+catalogue==2.0.10
 certifi==2024.7.4
 cffi==1.17.0
 charset-normalizer==3.3.2
 click==8.1.7
+cloudpathlib==0.19.0
+confection==0.1.5
 cryptography==43.0.0
+cymem==2.0.8
 deprecation==2.1.0
 dnspython==2.6.1
 dparse==0.6.4b0
 ecdsa==0.19.0
 email_validator==2.2.0
+en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl#sha256=1932429db727d4bff3deed6b34cfc05df17794f4a52eeb26cf8928f7c1a0fb85
 fastapi==0.112.0
 filelock==3.12.4
 frozenlist==1.4.1
@@ -28,17 +34,24 @@ hyperframe==6.0.1
 idna==3.7
 Jinja2==3.1.4
 joblib==1.4.2
+langcodes==3.4.1
+language_data==1.2.0
+lingua-language-detector==2.0.2
+marisa-trie==1.2.0
 markdown-it-py==3.0.0
 MarkupSafe==2.1.5
 marshmallow==3.22.0
 mdurl==0.1.2
 multidict==6.0.5
+murmurhash==1.0.10
 nltk==3.9.1
+numpy==2.0.2
 outscraper==5.2.1
 packaging==24.1
 pip-autoremove==0.10.0
 pipdeptree==2.23.1
 postgrest==0.16.11
+preshed==3.0.9
 psutil==6.0.0
 pyasn1==0.6.0
 pycparser==2.22
@@ -61,18 +74,28 @@ safety-schemas==0.0.5
 setuptools==73.0.1
 shellingham==1.5.4
 six==1.16.0
+smart-open==7.0.5
 sniffio==1.3.1
+spacy==3.8.2
+spacy-legacy==3.0.12
+spacy-loggers==1.0.5
+srsly==2.4.8
 starlette==0.37.2
 storage3==0.7.7
 StrEnum==0.4.15
 supabase==2.7.3
 supafunc==0.5.1
 textblob==0.18.0.post0
+thinc==8.3.2
 tqdm==4.66.5
 typer==0.12.5
 typing_extensions==4.12.2
 urllib3==2.2.2
 uvicorn==0.30.5
+vaderSentiment==3.3.2
+wasabi==1.1.3
+weasel==0.4.1
 websockets==12.0
+wrapt==1.16.0
 yarl==1.9.4
 zipp==3.20.1

frontend/src/app/restaurant/[id]/page.tsx

@@ -56,11 +56,11 @@ const RestaurantPage: React.FC = () => {
         }
 
         if (data.average_sentiment !== undefined) {
-            const scaledSentiment = ((data.average_sentiment + 1) / 2) * 10;
-            setAverageSentiment(scaledSentiment);
-        } else {
-            setAverageSentiment(null);
-        }
+          setAverageSentiment(data.average_sentiment);  // No additional scaling needed
+      } else {
+          setAverageSentiment(null);
+      }
+
     } catch (error) {
         console.error('Failed to fetch reviews:', error);
     } finally {