emotion-analysis 0.1.6

A package for emotion detection using transformers-based models.

Emotion Analysis Package

This package provides tools for emotion detection using transformer-based models. It includes functionalities for preprocessing data, generating word clouds, visualizing statistics, and evaluating model performance.

Installation

To install the package, you can use pip:

pip install emotion-analysis

Usage

Data Preprocessing

First, you need to preprocess your datasets. You can use the upload_datasets function to load your data. Here is the full testing code. For example:

from emotion_analysis.data_preprocessing import *
from emotion_analysis.tweets_embedding import *
from emotion_analysis.frnn import *
from emotion_analysis.bert_pcc_score_emotions import *
from emotion_analysis.roberta_pcc_score_emotions import *

anger_train, anger_dev, anger_data, anger_test = upload_datasets(
    "/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-anger-dev.txt",
    "/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/EI-oc-En-anger-train.txt",
    "/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-anger-test.txt",
)

fear_train, fear_dev, fear_data, fear_test = upload_datasets(
    "/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/EI-oc-En-fear-train.txt",
    "/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-fear-dev.txt",
    "/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-fear-test.txt",
)

joy_train, joy_dev, joy_data, joy_test = upload_datasets(
    "/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/EI-oc-En-joy-train.txt",
    "/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-joy-dev.txt",
    "/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-joy-test.txt",
)

sad_train, sad_dev, sad_data, sad_test = upload_datasets(
    "/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/EI-oc-En-sadness-train.txt",
    "/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-sadness-dev.txt",
    "/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-sadness-test.txt",
)


generate_wordclouds(anger_train, anger_dev, anger_data, anger_test, "Anger")
plot_statistics(anger_train, anger_dev, anger_data, anger_test, "Anger")
plot_additional_statistics(anger_train, anger_dev, anger_test, "Anger")
plot_top_n_words(anger_train, n=20, title="Top 20 Frequent Words in Anger Train Data")
plot_ngrams_frequency(
    anger_train, n=2, top_n=10, title="Bigram Frequency in Anger Train Data"
)
plot_ngrams_frequency(
    anger_train, n=3, top_n=10, title="Trigram Frequency in Anger Train Data"
)

generate_wordclouds(joy_train, joy_dev, joy_data, joy_test, "Joy")
plot_statistics(joy_train, joy_dev, joy_data, joy_test, "Joy")
plot_additional_statistics(joy_train, joy_dev, joy_test, "Joy")
plot_top_n_words(joy_train, n=20, title="Top 20 Frequent Words in Joy Train Data")
plot_ngrams_frequency(
    joy_train, n=2, top_n=10, title="Bigram Frequency in Joy Train Data"
)
plot_ngrams_frequency(
    joy_train, n=3, top_n=10, title="Trigram Frequency in Joy Train Data"
)

generate_wordclouds(sad_train, sad_dev, sad_data, sad_test, "Sadness")
plot_statistics(sad_train, sad_dev, sad_data, sad_test, "Sadness")
plot_additional_statistics(sad_train, sad_dev, sad_test, "Sadness")
plot_top_n_words(sad_train, n=20, title="Top 20 Frequent Words in Sadness Train Data")
plot_ngrams_frequency(
    sad_train, n=2, top_n=10, title="Bigram Frequency in Sadness Train Data"
)
plot_ngrams_frequency(
    sad_train, n=3, top_n=10, title="Trigram Frequency in Sadness Train Data"
)

generate_wordclouds(fear_train, fear_dev, fear_data, fear_test, "Fear")
plot_statistics(fear_train, fear_dev, fear_data, fear_test, "Fear")
plot_additional_statistics(fear_train, fear_dev, fear_test, "Fear")
plot_top_n_words(fear_train, n=20, title="Top 20 Frequent Words in Fear Train Data")
plot_ngrams_frequency(
    fear_train, n=2, top_n=10, title="Bigram Frequency in Fear Train Data"
)
plot_ngrams_frequency(
    fear_train, n=3, top_n=10, title="Trigram Frequency in Fear Train Data"
)


plot_dataset_statistics([anger_data, joy_data, sad_data, fear_data])

print("vector Bert")

anger_data["Vector_bert"] = anger_data["Tweet"].apply(get_vector_bert)
anger_test["Vector_bert"] = anger_test["Tweet"].apply(get_vector_bert)

joy_data["Vector_bert"] = joy_data["Tweet"].apply(get_vector_bert)
joy_test["Vector_bert"] = joy_test["Tweet"].apply(get_vector_bert)

sad_data["Vector_bert"] = sad_data["Tweet"].apply(get_vector_bert)
sad_test["Vector_bert"] = sad_test["Tweet"].apply(get_vector_bert)

fear_data["Vector_bert"] = fear_data["Tweet"].apply(get_vector_bert)
fear_test["Vector_bert"] = fear_test["Tweet"].apply(get_vector_bert)

print("1st done")

anger_data["Vector_bert_cl"] = anger_data["Cleaned_tweet"].apply(get_vector_bert)
anger_test["Vector_bert_cl"] = anger_test["Cleaned_tweet"].apply(get_vector_bert)

joy_data["Vector_bert_cl"] = joy_data["Cleaned_tweet"].apply(get_vector_bert)
joy_test["Vector_bert_cl"] = joy_test["Cleaned_tweet"].apply(get_vector_bert)

sad_data["Vector_bert_cl"] = sad_data["Cleaned_tweet"].apply(get_vector_bert)
sad_test["Vector_bert_cl"] = sad_test["Cleaned_tweet"].apply(get_vector_bert)

fear_data["Vector_bert_cl"] = fear_data["Cleaned_tweet"].apply(get_vector_bert)
fear_test["Vector_bert_cl"] = fear_test["Cleaned_tweet"].apply(get_vector_bert)

print("2nd done")

# BERT
# With raw tweets

anger_data["Vector_bert_cl_ws"] = anger_data["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_bert
)
anger_test["Vector_bert_cl_ws"] = anger_test["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_bert
)

joy_data["Vector_bert_cl_ws"] = joy_data["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_bert
)
joy_test["Vector_bert_cl_ws"] = joy_test["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_bert
)

sad_data["Vector_bert_cl_ws"] = sad_data["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_bert
)
sad_test["Vector_bert_cl_ws"] = sad_test["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_bert
)

fear_data["Vector_bert_cl_ws"] = fear_data["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_bert
)
fear_test["Vector_bert_cl_ws"] = fear_test["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_bert
)


anger_data["Vector_roberta"] = anger_data["Tweet"].apply(get_vector_roberta)
anger_test["Vector_roberta"] = anger_test["Tweet"].apply(get_vector_roberta)

joy_data["Vector_roberta"] = joy_data["Tweet"].apply(get_vector_roberta)
joy_test["Vector_roberta"] = joy_test["Tweet"].apply(get_vector_roberta)

sad_data["Vector_roberta"] = sad_data["Tweet"].apply(get_vector_roberta)
sad_test["Vector_roberta"] = sad_test["Tweet"].apply(get_vector_roberta)

fear_data["Vector_roberta"] = fear_data["Tweet"].apply(get_vector_roberta)
fear_test["Vector_roberta"] = fear_test["Tweet"].apply(get_vector_roberta)

print("1st done")

anger_data["Vector_roberta_cl"] = anger_data["Cleaned_tweet"].apply(get_vector_roberta)
anger_test["Vector_roberta_cl"] = anger_test["Cleaned_tweet"].apply(get_vector_roberta)

joy_data["Vector_roberta_cl"] = joy_data["Cleaned_tweet"].apply(get_vector_roberta)
joy_test["Vector_roberta_cl"] = joy_test["Cleaned_tweet"].apply(get_vector_roberta)

sad_data["Vector_roberta_cl"] = sad_data["Cleaned_tweet"].apply(get_vector_roberta)
sad_test["Vector_roberta_cl"] = sad_test["Cleaned_tweet"].apply(get_vector_roberta)

fear_data["Vector_roberta_cl"] = fear_data["Cleaned_tweet"].apply(get_vector_roberta)
fear_test["Vector_roberta_cl"] = fear_test["Cleaned_tweet"].apply(get_vector_roberta)

print("2nd done")

# roberta
# With raw tweets

anger_data["Vector_roberta_cl_ws"] = anger_data["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_roberta
)
anger_test["Vector_roberta_cl_ws"] = anger_test["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_roberta
)

joy_data["Vector_roberta_cl_ws"] = joy_data["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_roberta
)
joy_test["Vector_roberta_cl_ws"] = joy_test["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_roberta
)

sad_data["Vector_roberta_cl_ws"] = sad_data["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_roberta
)
sad_test["Vector_roberta_cl_ws"] = sad_test["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_roberta
)

fear_data["Vector_roberta_cl_ws"] = fear_data["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_roberta
)
fear_test["Vector_roberta_cl_ws"] = fear_test["Cleaned_tweet_wt_stopwords"].apply(
    get_vector_roberta
)


anger_pcc_scores = cross_validation_ensemble_owa(
    anger_data.head(10),
    vector_names=["Vector_roberta_cl", "Vector_roberta", "Vector_roberta_cl_ws"],
    NNeighbours=[5, 5],
    lower=[1, 1],
    upper=[1, 1],
    alpha=0.5,
    cv=5,
)
print("anger_PCC Scores:", np.mean(anger_pcc_scores))

joy_pcc_scores = cross_validation_ensemble_owa(
    joy_data.head(10),
    vector_names=["Vector_roberta_cl", "Vector_roberta", "Vector_roberta_cl_ws"],
    NNeighbours=[5, 5],
    lower=[1, 1],
    upper=[1, 1],
    alpha=0.5,
    cv=5,
)
print("joy_PCC Scores:", np.mean(joy_pcc_scores))

fear_pcc_scores = cross_validation_ensemble_owa(
    fear_data.head(10),
    vector_names=["Vector_roberta_cl", "Vector_roberta", "Vector_roberta_cl_ws"],
    NNeighbours=[5, 5],
    lower=[1, 1],
    upper=[1, 1],
    alpha=0.5,
    cv=5,
)
print("fear_PCC Scores:", np.mean(fear_pcc_scores))

sad_pcc_scores = cross_validation_ensemble_owa(
    sad_data.head(10),
    vector_names=["Vector_roberta_cl", "Vector_roberta", "Vector_roberta_cl_ws"],
    NNeighbours=[5, 5],
    lower=[1, 1],
    upper=[1, 1],
    alpha=0.5,
    cv=5,
)
print("sad_PCC Scores:", np.mean(sad_pcc_scores))


anger_pcc_scores = np.mean(anger_pcc_scores)
joy_pcc_scores = np.mean(joy_pcc_scores)
fear_pcc_scores = np.mean(fear_pcc_scores)
sad_pcc_scores = np.mean(sad_pcc_scores)
avg_roberta_emotions_pcc = (
    anger_pcc_scores + joy_pcc_scores + fear_pcc_scores + sad_pcc_scores
) / 4
print("Final PCC Score roberta :", avg_roberta_emotions_pcc)


import os
import matplotlib.pyplot as plt

# Emotion labels
emotion_labels = ["Anger", "Joy", "Fear", "Sadness"]

# Vector types
vector_types = ["Vector_roberta_cl", "Vector_roberta", "Vector_roberta_cl_ws", "bb"]

# Mean PCC scores for each vector type
mean_pcc_scores = [
    np.mean(anger_pcc_scores),
    np.mean(joy_pcc_scores),
    np.mean(fear_pcc_scores),
    np.mean(sad_pcc_scores),
]

plt.figure(figsize=(10, 6))

bars = plt.bar(vector_types, mean_pcc_scores, color="skyblue")

plt.xlabel("Vector Type")
plt.ylabel("Mean PCC Score")
plt.title("Mean PCC Scores for Different Vector Types")
plt.xticks(rotation=45)
plt.grid(axis="y")

# Set the emotion labels as tick labels on the x-axis
plt.xticks(ticks=vector_types, labels=emotion_labels)

# Add value labels on top of each bar
for bar, score in zip(bars, mean_pcc_scores):
    plt.text(
        bar.get_x() + bar.get_width() / 2,
        bar.get_height() + 0.01,
        round(score, 4),
        ha="center",
        va="bottom",
        fontweight="bold",
    )

# Create the directory if it doesn't exist
plots_dir = "plots/roberta_pcc_score"
os.makedirs(plots_dir, exist_ok=True)

# Save the figure
plt.savefig(os.path.join(plots_dir, "roberta_pcc_scores.png"))

print("roberta Base Vector Done!")


anger_pcc_scores = cross_validation_ensemble_owa(
    anger_data.head(10),
    vector_names=["Vector_bert_cl", "Vector_bert", "Vector_bert_cl_ws"],
    NNeighbours=[5, 5],
    lower=[1, 1],
    upper=[1, 1],
    alpha=0.5,
    cv=5,
)
print("anger_PCC Scores:", np.mean(anger_pcc_scores))

joy_pcc_scores = cross_validation_ensemble_owa(
    joy_data.head(10),
    vector_names=["Vector_bert_cl", "Vector_bert", "Vector_bert_cl_ws"],
    NNeighbours=[5, 5],
    lower=[1, 1],
    upper=[1, 1],
    alpha=0.5,
    cv=5,
)
print("joy_PCC Scores:", np.mean(joy_pcc_scores))

fear_pcc_scores = cross_validation_ensemble_owa(
    fear_data.head(10),
    vector_names=["Vector_bert_cl", "Vector_bert", "Vector_bert_cl_ws"],
    NNeighbours=[5, 5],
    lower=[1, 1],
    upper=[1, 1],
    alpha=0.5,
    cv=5,
)
print("fear_PCC Scores:", np.mean(fear_pcc_scores))

sad_pcc_scores = cross_validation_ensemble_owa(
    sad_data.head(10),
    vector_names=["Vector_bert_cl", "Vector_bert", "Vector_bert_cl_ws"],
    NNeighbours=[5, 5],
    lower=[1, 1],
    upper=[1, 1],
    alpha=0.5,
    cv=5,
)
print("sad_PCC Scores:", np.mean(sad_pcc_scores))


anger_pcc_scores = np.mean(anger_pcc_scores)
joy_pcc_scores = np.mean(joy_pcc_scores)
fear_pcc_scores = np.mean(fear_pcc_scores)
sad_pcc_scores = np.mean(sad_pcc_scores)
avg_bert_emotions_pcc = (
    anger_pcc_scores + joy_pcc_scores + fear_pcc_scores + sad_pcc_scores
) / 4
print("Final PCC Score Bert :", avg_bert_emotions_pcc)


import os
import matplotlib.pyplot as plt

# Emotion labels
emotion_labels = ["Anger", "Joy", "Fear", "Sadness"]

# Vector types
vector_types = ["Vector_bert_cl", "Vector_bert", "Vector_bert_cl_ws", "bb"]

# Mean PCC scores for each vector type
mean_pcc_scores = [
    np.mean(anger_pcc_scores),
    np.mean(joy_pcc_scores),
    np.mean(fear_pcc_scores),
    np.mean(sad_pcc_scores),
]

plt.figure(figsize=(10, 6))

bars = plt.bar(vector_types, mean_pcc_scores, color="skyblue")

plt.xlabel("Vector Type")
plt.ylabel("Mean PCC Score")
plt.title("Mean PCC Scores for Different Vector Types")
plt.xticks(rotation=45)
plt.grid(axis="y")

# Set the emotion labels as tick labels on the x-axis
plt.xticks(ticks=vector_types, labels=emotion_labels)

# Add value labels on top of each bar
for bar, score in zip(bars, mean_pcc_scores):
    plt.text(
        bar.get_x() + bar.get_width() / 2,
        bar.get_height() + 0.01,
        round(score, 4),
        ha="center",
        va="bottom",
        fontweight="bold",
    )

# Create the directory if it doesn't exist
plots_dir = "plots/bert_pcc_score"
os.makedirs(plots_dir, exist_ok=True)

# Save the figure
plt.savefig(os.path.join(plots_dir, "bert_pcc_scores.png"))

print("Bert Base Vector Done!")


print(
    "Thanks For Your Patence. Code Run Succesfull Checkout Plot Folder to see Results!"
)

FRNN OWA Weights and PCC Score Calculation

Our package implements the FRNN algorithm with OWA weights for evaluating model performance. Here's a brief overview of how the PCC scores are calculated:

1. FRNN Algorithm: The FRNN algorithm is used to predict emotion labels based on the nearest neighbors in the feature space.

2. OWA Weights: OWA weights are applied to the nearest neighbors' emotion labels to compute a weighted average. These weights are determined based on the degree of importance assigned to each neighbor.

3. PCC Score Calculation: After predicting emotion labels for the test data using the FRNN algorithm, the Pearson Correlation Coefficient (PCC) is calculated to measure the correlation between the predicted and true emotion labels. A higher PCC score indicates better model performance.

For detailed implementation and usage examples, please refer to the documentation and codebase.

Benefits

Easy-to-Use

Our package offers a user-friendly interface with simple function calls, making it accessible for both beginners and experienced users.

Transformer-Based Models

We leverage state-of-the-art transformer-based models for emotion detection, ensuring high accuracy and robust performance.

Comprehensive Functionality

From data preprocessing to model evaluation, our package provides a wide range of functionalities to streamline the entire process of emotion analysis.

Visualization Tools

With built-in visualization tools such as word clouds and statistical plots, users can gain insights into their data and model performance at a glance.

Modular and Extensible

Our package is designed with modularity in mind, allowing users to easily extend its functionality or integrate it into existing workflows.

Active Development and Support

We are committed to continuously improving our package and providing prompt support to our users through GitHub issues and pull requests.

Open Source and Free

Our package is open source and freely available under the MIT License, allowing users to use, modify, and distribute it without any restrictions.

Contact

For any inquiries, feedback, or support requests, please feel free to reach out:

• Ahsan Tariq
  • Email: [e-mail]
  • GitHub: ahsantariq7