src/semantic_analysis.py

# # Emotional classification - we are gonna do a fine tuning in order to get an llm that will do emotional classification and how it does that? check and understand in the video

# %%
import pandas as pd

books = pd.read_csv("books_with_categories.csv")

# %%
from transformers import pipeline
classifier = pipeline("text-classification",
                      model="j-hartmann/emotion-english-distilroberta-base",
                      top_k = None,
                      device=0)
classifier("I love this!")

# %%
books["description"][0]

# %%
classifier(books["description"][0])

# %%
classifier(books["description"][0].split("."))

# %%
sentences = books["description"][0].split(".")
predictions = classifier(sentences)
sentences[0]

# %%
predictions[0]

# %%
sentences[3]

# %%
predictions[3]

# %%
predictions

# %%
sorted(predictions[0], key=lambda x: x["label"])

# %%
import numpy as np

emotion_labels = ["anger", "disgust", "fear", "joy", "sadness", "surprise", "neutral"]
isbn = []
emotion_scores = {label: [] for label in emotion_labels}

def calculate_max_emotion_scores(predictions):
    per_emotion_scores = {label: [] for label in emotion_labels}
    for prediction in predictions:
        sorted_predictions = sorted(prediction, key=lambda x: x["label"])
        for index, label in enumerate(emotion_labels):
            per_emotion_scores[label].append(sorted_predictions[index]["score"])
    return {label: np.max(scores) for label, scores in per_emotion_scores.items()}

# %%
for i in range(10):
    isbn.append(books["isbn13"][i])
    sentences = books["description"][i].split(".")
    predictions = classifier(sentences)
    max_scores = calculate_max_emotion_scores(predictions)
    for label in emotion_labels:
        emotion_scores[label].append(max_scores[label])
emotion_scores

# %%
from tqdm import tqdm

emotion_labels = ["anger", "disgust", "fear", "joy", "sadness", "surprise", "neutral"]
isbn = []
emotion_scores = {label: [] for label in emotion_labels}

for i in tqdm(range(len(books))):
    isbn.append(books["isbn13"][i])
    sentences = books["description"][i].split(".")
    predictions = classifier(sentences)
    max_scores = calculate_max_emotion_scores(predictions)
    for label in emotion_labels:
        emotion_scores[label].append(max_scores[label])

# %%
emotions_df = pd.DataFrame(emotion_scores)
emotions_df["isbn13"] = isbn
emotions_df

# %%
books = pd.merge(books, emotions_df, on = "isbn13")
books


# %%
books.to_csv("books_with_emotions.csv", index = False)