# 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:
```bash
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:** ahsantariq0724@email.com
- **GitHub:** [ahsantariq7](https://github.com/ahsantariq7)
Raw data
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"keywords": "emotion analysis transformers machine learning, engr_ahsan_tariq_0724, bert_vector, roberta_vector, nlp, text emotion analysis, emotion analysis",
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"description": "# Emotion Analysis Package\n\nThis 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.\n\n## Installation\n\nTo install the package, you can use pip:\n\n`pip install emotion-analysis`\n\n## Usage\n\n### Data Preprocessing\n\nFirst, 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:\n\n\n```bash\nfrom emotion_analysis.data_preprocessing import *\nfrom emotion_analysis.tweets_embedding import *\nfrom emotion_analysis.frnn import *\nfrom emotion_analysis.bert_pcc_score_emotions import *\nfrom emotion_analysis.roberta_pcc_score_emotions import *\n\nanger_train, anger_dev, anger_data, anger_test = upload_datasets(\n \"/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-anger-dev.txt\",\n \"/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/EI-oc-En-anger-train.txt\",\n \"/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-anger-test.txt\",\n)\n\nfear_train, fear_dev, fear_data, fear_test = upload_datasets(\n \"/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/EI-oc-En-fear-train.txt\",\n \"/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-fear-dev.txt\",\n \"/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-fear-test.txt\",\n)\n\njoy_train, joy_dev, joy_data, joy_test = upload_datasets(\n \"/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/EI-oc-En-joy-train.txt\",\n \"/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-joy-dev.txt\",\n \"/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-joy-test.txt\",\n)\n\nsad_train, sad_dev, sad_data, sad_test = upload_datasets(\n \"/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/EI-oc-En-sadness-train.txt\",\n \"/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-sadness-dev.txt\",\n \"/home/ahsan-pmylsp/Desktop/frnn_emotion_detection-main/update/Frnn_Emotion/emotion_analysis/dataset/2018-EI-oc-En-sadness-test.txt\",\n)\n\n\ngenerate_wordclouds(anger_train, anger_dev, anger_data, anger_test, \"Anger\")\nplot_statistics(anger_train, anger_dev, anger_data, anger_test, \"Anger\")\nplot_additional_statistics(anger_train, anger_dev, anger_test, \"Anger\")\nplot_top_n_words(anger_train, n=20, title=\"Top 20 Frequent Words in Anger Train Data\")\nplot_ngrams_frequency(\n anger_train, n=2, top_n=10, title=\"Bigram Frequency in Anger Train Data\"\n)\nplot_ngrams_frequency(\n anger_train, n=3, top_n=10, title=\"Trigram Frequency in Anger Train Data\"\n)\n\ngenerate_wordclouds(joy_train, joy_dev, joy_data, joy_test, \"Joy\")\nplot_statistics(joy_train, joy_dev, joy_data, joy_test, \"Joy\")\nplot_additional_statistics(joy_train, joy_dev, joy_test, \"Joy\")\nplot_top_n_words(joy_train, n=20, title=\"Top 20 Frequent Words in Joy Train Data\")\nplot_ngrams_frequency(\n joy_train, n=2, top_n=10, title=\"Bigram Frequency in Joy Train Data\"\n)\nplot_ngrams_frequency(\n joy_train, n=3, top_n=10, title=\"Trigram Frequency in Joy Train Data\"\n)\n\ngenerate_wordclouds(sad_train, sad_dev, sad_data, sad_test, \"Sadness\")\nplot_statistics(sad_train, sad_dev, sad_data, sad_test, \"Sadness\")\nplot_additional_statistics(sad_train, sad_dev, sad_test, \"Sadness\")\nplot_top_n_words(sad_train, n=20, title=\"Top 20 Frequent Words in Sadness Train Data\")\nplot_ngrams_frequency(\n sad_train, n=2, top_n=10, title=\"Bigram Frequency in Sadness Train Data\"\n)\nplot_ngrams_frequency(\n sad_train, n=3, top_n=10, title=\"Trigram Frequency in Sadness Train Data\"\n)\n\ngenerate_wordclouds(fear_train, fear_dev, fear_data, fear_test, \"Fear\")\nplot_statistics(fear_train, fear_dev, fear_data, fear_test, \"Fear\")\nplot_additional_statistics(fear_train, fear_dev, fear_test, \"Fear\")\nplot_top_n_words(fear_train, n=20, title=\"Top 20 Frequent Words in Fear Train Data\")\nplot_ngrams_frequency(\n fear_train, n=2, top_n=10, title=\"Bigram Frequency in Fear Train Data\"\n)\nplot_ngrams_frequency(\n fear_train, n=3, top_n=10, title=\"Trigram Frequency in Fear Train Data\"\n)\n\n\nplot_dataset_statistics([anger_data, joy_data, sad_data, fear_data])\n\nprint(\"vector Bert\")\n\nanger_data[\"Vector_bert\"] = anger_data[\"Tweet\"].apply(get_vector_bert)\nanger_test[\"Vector_bert\"] = anger_test[\"Tweet\"].apply(get_vector_bert)\n\njoy_data[\"Vector_bert\"] = joy_data[\"Tweet\"].apply(get_vector_bert)\njoy_test[\"Vector_bert\"] = joy_test[\"Tweet\"].apply(get_vector_bert)\n\nsad_data[\"Vector_bert\"] = sad_data[\"Tweet\"].apply(get_vector_bert)\nsad_test[\"Vector_bert\"] = sad_test[\"Tweet\"].apply(get_vector_bert)\n\nfear_data[\"Vector_bert\"] = fear_data[\"Tweet\"].apply(get_vector_bert)\nfear_test[\"Vector_bert\"] = fear_test[\"Tweet\"].apply(get_vector_bert)\n\nprint(\"1st done\")\n\nanger_data[\"Vector_bert_cl\"] = anger_data[\"Cleaned_tweet\"].apply(get_vector_bert)\nanger_test[\"Vector_bert_cl\"] = anger_test[\"Cleaned_tweet\"].apply(get_vector_bert)\n\njoy_data[\"Vector_bert_cl\"] = joy_data[\"Cleaned_tweet\"].apply(get_vector_bert)\njoy_test[\"Vector_bert_cl\"] = joy_test[\"Cleaned_tweet\"].apply(get_vector_bert)\n\nsad_data[\"Vector_bert_cl\"] = sad_data[\"Cleaned_tweet\"].apply(get_vector_bert)\nsad_test[\"Vector_bert_cl\"] = sad_test[\"Cleaned_tweet\"].apply(get_vector_bert)\n\nfear_data[\"Vector_bert_cl\"] = fear_data[\"Cleaned_tweet\"].apply(get_vector_bert)\nfear_test[\"Vector_bert_cl\"] = fear_test[\"Cleaned_tweet\"].apply(get_vector_bert)\n\nprint(\"2nd done\")\n\n# BERT\n# With raw tweets\n\nanger_data[\"Vector_bert_cl_ws\"] = anger_data[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_bert\n)\nanger_test[\"Vector_bert_cl_ws\"] = anger_test[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_bert\n)\n\njoy_data[\"Vector_bert_cl_ws\"] = joy_data[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_bert\n)\njoy_test[\"Vector_bert_cl_ws\"] = joy_test[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_bert\n)\n\nsad_data[\"Vector_bert_cl_ws\"] = sad_data[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_bert\n)\nsad_test[\"Vector_bert_cl_ws\"] = sad_test[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_bert\n)\n\nfear_data[\"Vector_bert_cl_ws\"] = fear_data[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_bert\n)\nfear_test[\"Vector_bert_cl_ws\"] = fear_test[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_bert\n)\n\n\nanger_data[\"Vector_roberta\"] = anger_data[\"Tweet\"].apply(get_vector_roberta)\nanger_test[\"Vector_roberta\"] = anger_test[\"Tweet\"].apply(get_vector_roberta)\n\njoy_data[\"Vector_roberta\"] = joy_data[\"Tweet\"].apply(get_vector_roberta)\njoy_test[\"Vector_roberta\"] = joy_test[\"Tweet\"].apply(get_vector_roberta)\n\nsad_data[\"Vector_roberta\"] = sad_data[\"Tweet\"].apply(get_vector_roberta)\nsad_test[\"Vector_roberta\"] = sad_test[\"Tweet\"].apply(get_vector_roberta)\n\nfear_data[\"Vector_roberta\"] = fear_data[\"Tweet\"].apply(get_vector_roberta)\nfear_test[\"Vector_roberta\"] = fear_test[\"Tweet\"].apply(get_vector_roberta)\n\nprint(\"1st done\")\n\nanger_data[\"Vector_roberta_cl\"] = anger_data[\"Cleaned_tweet\"].apply(get_vector_roberta)\nanger_test[\"Vector_roberta_cl\"] = anger_test[\"Cleaned_tweet\"].apply(get_vector_roberta)\n\njoy_data[\"Vector_roberta_cl\"] = joy_data[\"Cleaned_tweet\"].apply(get_vector_roberta)\njoy_test[\"Vector_roberta_cl\"] = joy_test[\"Cleaned_tweet\"].apply(get_vector_roberta)\n\nsad_data[\"Vector_roberta_cl\"] = sad_data[\"Cleaned_tweet\"].apply(get_vector_roberta)\nsad_test[\"Vector_roberta_cl\"] = sad_test[\"Cleaned_tweet\"].apply(get_vector_roberta)\n\nfear_data[\"Vector_roberta_cl\"] = fear_data[\"Cleaned_tweet\"].apply(get_vector_roberta)\nfear_test[\"Vector_roberta_cl\"] = fear_test[\"Cleaned_tweet\"].apply(get_vector_roberta)\n\nprint(\"2nd done\")\n\n# roberta\n# With raw tweets\n\nanger_data[\"Vector_roberta_cl_ws\"] = anger_data[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_roberta\n)\nanger_test[\"Vector_roberta_cl_ws\"] = anger_test[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_roberta\n)\n\njoy_data[\"Vector_roberta_cl_ws\"] = joy_data[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_roberta\n)\njoy_test[\"Vector_roberta_cl_ws\"] = joy_test[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_roberta\n)\n\nsad_data[\"Vector_roberta_cl_ws\"] = sad_data[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_roberta\n)\nsad_test[\"Vector_roberta_cl_ws\"] = sad_test[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_roberta\n)\n\nfear_data[\"Vector_roberta_cl_ws\"] = fear_data[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_roberta\n)\nfear_test[\"Vector_roberta_cl_ws\"] = fear_test[\"Cleaned_tweet_wt_stopwords\"].apply(\n get_vector_roberta\n)\n\n\nanger_pcc_scores = cross_validation_ensemble_owa(\n anger_data.head(10),\n vector_names=[\"Vector_roberta_cl\", \"Vector_roberta\", \"Vector_roberta_cl_ws\"],\n NNeighbours=[5, 5],\n lower=[1, 1],\n upper=[1, 1],\n alpha=0.5,\n cv=5,\n)\nprint(\"anger_PCC Scores:\", np.mean(anger_pcc_scores))\n\njoy_pcc_scores = cross_validation_ensemble_owa(\n joy_data.head(10),\n vector_names=[\"Vector_roberta_cl\", \"Vector_roberta\", \"Vector_roberta_cl_ws\"],\n NNeighbours=[5, 5],\n lower=[1, 1],\n upper=[1, 1],\n alpha=0.5,\n cv=5,\n)\nprint(\"joy_PCC Scores:\", np.mean(joy_pcc_scores))\n\nfear_pcc_scores = cross_validation_ensemble_owa(\n fear_data.head(10),\n vector_names=[\"Vector_roberta_cl\", \"Vector_roberta\", \"Vector_roberta_cl_ws\"],\n NNeighbours=[5, 5],\n lower=[1, 1],\n upper=[1, 1],\n alpha=0.5,\n cv=5,\n)\nprint(\"fear_PCC Scores:\", np.mean(fear_pcc_scores))\n\nsad_pcc_scores = cross_validation_ensemble_owa(\n sad_data.head(10),\n vector_names=[\"Vector_roberta_cl\", \"Vector_roberta\", \"Vector_roberta_cl_ws\"],\n NNeighbours=[5, 5],\n lower=[1, 1],\n upper=[1, 1],\n alpha=0.5,\n cv=5,\n)\nprint(\"sad_PCC Scores:\", np.mean(sad_pcc_scores))\n\n\nanger_pcc_scores = np.mean(anger_pcc_scores)\njoy_pcc_scores = np.mean(joy_pcc_scores)\nfear_pcc_scores = np.mean(fear_pcc_scores)\nsad_pcc_scores = np.mean(sad_pcc_scores)\navg_roberta_emotions_pcc = (\n anger_pcc_scores + joy_pcc_scores + fear_pcc_scores + sad_pcc_scores\n) / 4\nprint(\"Final PCC Score roberta :\", avg_roberta_emotions_pcc)\n\n\nimport os\nimport matplotlib.pyplot as plt\n\n# Emotion labels\nemotion_labels = [\"Anger\", \"Joy\", \"Fear\", \"Sadness\"]\n\n# Vector types\nvector_types = [\"Vector_roberta_cl\", \"Vector_roberta\", \"Vector_roberta_cl_ws\", \"bb\"]\n\n# Mean PCC scores for each vector type\nmean_pcc_scores = [\n np.mean(anger_pcc_scores),\n np.mean(joy_pcc_scores),\n np.mean(fear_pcc_scores),\n np.mean(sad_pcc_scores),\n]\n\nplt.figure(figsize=(10, 6))\n\nbars = plt.bar(vector_types, mean_pcc_scores, color=\"skyblue\")\n\nplt.xlabel(\"Vector Type\")\nplt.ylabel(\"Mean PCC Score\")\nplt.title(\"Mean PCC Scores for Different Vector Types\")\nplt.xticks(rotation=45)\nplt.grid(axis=\"y\")\n\n# Set the emotion labels as tick labels on the x-axis\nplt.xticks(ticks=vector_types, labels=emotion_labels)\n\n# Add value labels on top of each bar\nfor bar, score in zip(bars, mean_pcc_scores):\n plt.text(\n bar.get_x() + bar.get_width() / 2,\n bar.get_height() + 0.01,\n round(score, 4),\n ha=\"center\",\n va=\"bottom\",\n fontweight=\"bold\",\n )\n\n# Create the directory if it doesn't exist\nplots_dir = \"plots/roberta_pcc_score\"\nos.makedirs(plots_dir, exist_ok=True)\n\n# Save the figure\nplt.savefig(os.path.join(plots_dir, \"roberta_pcc_scores.png\"))\n\nprint(\"roberta Base Vector Done!\")\n\n\nanger_pcc_scores = cross_validation_ensemble_owa(\n anger_data.head(10),\n vector_names=[\"Vector_bert_cl\", \"Vector_bert\", \"Vector_bert_cl_ws\"],\n NNeighbours=[5, 5],\n lower=[1, 1],\n upper=[1, 1],\n alpha=0.5,\n cv=5,\n)\nprint(\"anger_PCC Scores:\", np.mean(anger_pcc_scores))\n\njoy_pcc_scores = cross_validation_ensemble_owa(\n joy_data.head(10),\n vector_names=[\"Vector_bert_cl\", \"Vector_bert\", \"Vector_bert_cl_ws\"],\n NNeighbours=[5, 5],\n lower=[1, 1],\n upper=[1, 1],\n alpha=0.5,\n cv=5,\n)\nprint(\"joy_PCC Scores:\", np.mean(joy_pcc_scores))\n\nfear_pcc_scores = cross_validation_ensemble_owa(\n fear_data.head(10),\n vector_names=[\"Vector_bert_cl\", \"Vector_bert\", \"Vector_bert_cl_ws\"],\n NNeighbours=[5, 5],\n lower=[1, 1],\n upper=[1, 1],\n alpha=0.5,\n cv=5,\n)\nprint(\"fear_PCC Scores:\", np.mean(fear_pcc_scores))\n\nsad_pcc_scores = cross_validation_ensemble_owa(\n sad_data.head(10),\n vector_names=[\"Vector_bert_cl\", \"Vector_bert\", \"Vector_bert_cl_ws\"],\n NNeighbours=[5, 5],\n lower=[1, 1],\n upper=[1, 1],\n alpha=0.5,\n cv=5,\n)\nprint(\"sad_PCC Scores:\", np.mean(sad_pcc_scores))\n\n\nanger_pcc_scores = np.mean(anger_pcc_scores)\njoy_pcc_scores = np.mean(joy_pcc_scores)\nfear_pcc_scores = np.mean(fear_pcc_scores)\nsad_pcc_scores = np.mean(sad_pcc_scores)\navg_bert_emotions_pcc = (\n anger_pcc_scores + joy_pcc_scores + fear_pcc_scores + sad_pcc_scores\n) / 4\nprint(\"Final PCC Score Bert :\", avg_bert_emotions_pcc)\n\n\nimport os\nimport matplotlib.pyplot as plt\n\n# Emotion labels\nemotion_labels = [\"Anger\", \"Joy\", \"Fear\", \"Sadness\"]\n\n# Vector types\nvector_types = [\"Vector_bert_cl\", \"Vector_bert\", \"Vector_bert_cl_ws\", \"bb\"]\n\n# Mean PCC scores for each vector type\nmean_pcc_scores = [\n np.mean(anger_pcc_scores),\n np.mean(joy_pcc_scores),\n np.mean(fear_pcc_scores),\n np.mean(sad_pcc_scores),\n]\n\nplt.figure(figsize=(10, 6))\n\nbars = plt.bar(vector_types, mean_pcc_scores, color=\"skyblue\")\n\nplt.xlabel(\"Vector Type\")\nplt.ylabel(\"Mean PCC Score\")\nplt.title(\"Mean PCC Scores for Different Vector Types\")\nplt.xticks(rotation=45)\nplt.grid(axis=\"y\")\n\n# Set the emotion labels as tick labels on the x-axis\nplt.xticks(ticks=vector_types, labels=emotion_labels)\n\n# Add value labels on top of each bar\nfor bar, score in zip(bars, mean_pcc_scores):\n plt.text(\n bar.get_x() + bar.get_width() / 2,\n bar.get_height() + 0.01,\n round(score, 4),\n ha=\"center\",\n va=\"bottom\",\n fontweight=\"bold\",\n )\n\n# Create the directory if it doesn't exist\nplots_dir = \"plots/bert_pcc_score\"\nos.makedirs(plots_dir, exist_ok=True)\n\n# Save the figure\nplt.savefig(os.path.join(plots_dir, \"bert_pcc_scores.png\"))\n\nprint(\"Bert Base Vector Done!\")\n\n\nprint(\n \"Thanks For Your Patence. Code Run Succesfull Checkout Plot Folder to see Results!\"\n)\n\n```\n\n## FRNN OWA Weights and PCC Score Calculation\n\nOur package implements the FRNN algorithm with OWA weights for evaluating model performance. Here's a brief overview of how the PCC scores are calculated:\n\n1. **FRNN Algorithm:** The FRNN algorithm is used to predict emotion labels based on the nearest neighbors in the feature space.\n\n2. **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.\n\n3. **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.\n\nFor detailed implementation and usage examples, please refer to the documentation and codebase.\n\n\n## Benefits\n\n### Easy-to-Use\n\nOur package offers a user-friendly interface with simple function calls, making it accessible for both beginners and experienced users.\n\n### Transformer-Based Models\n\nWe leverage state-of-the-art transformer-based models for emotion detection, ensuring high accuracy and robust performance.\n\n### Comprehensive Functionality\n\nFrom data preprocessing to model evaluation, our package provides a wide range of functionalities to streamline the entire process of emotion analysis.\n\n### Visualization Tools\n\nWith built-in visualization tools such as word clouds and statistical plots, users can gain insights into their data and model performance at a glance.\n\n### Modular and Extensible\n\nOur package is designed with modularity in mind, allowing users to easily extend its functionality or integrate it into existing workflows.\n\n### Active Development and Support\n\nWe are committed to continuously improving our package and providing prompt support to our users through GitHub issues and pull requests.\n\n### Open Source and Free\n\nOur package is open source and freely available under the MIT License, allowing users to use, modify, and distribute it without any restrictions.\n\n\n## Contact\n\nFor any inquiries, feedback, or support requests, please feel free to reach out:\n\n- **Ahsan Tariq**\n - **Email:** ahsantariq0724@email.com\n - **GitHub:** [ahsantariq7](https://github.com/ahsantariq7)\n\n\n",
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