Emotion Detection from Tweets Using Ensemble Models (1).pptx

Emotion Detection from Tweets Using Ensemble Models (1).pptx

1. Emotion Detection from Tweets Using Ensemble Models Team Prakash Babu Yandrapati Santoshachandra Rao Karanam Parnem Ruchith Reddy Srihith Rachakonda Yatarla Tharun Reddy Alla Bharath Teja GITAM University Hyderabad 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies

2. Table of contents Introduction Literature Survey Methodology Results analysis Conclusion 17-07-2024 Emotion Detection from Tweets Using Ensemble Models 2

3. Introduction Text emotion analysis, also known as sentiment analysis, determines the emotional tone behind a body of text. It uses natural language processing (NLP) and machine learning to extract subjective information. 17-07-2024 Emotion Detection from Tweets Using Ensemble Models 3

4. Introduction Cont.… 17-07-2024 Emotion Detection from Tweets Using Ensemble Models 4 Applications : Used in customer feedback analysis, social media monitoring, brand reputation management, and market research. It helps organizations understand public opinion, gauge customer satisfaction, and improve products and services. Techniques : Common techniques include rule-based approaches, machine learning models, and deep learning methods. Rule-based use dictionaries and linguistic rules, while machine learning relies on training data to learn patterns.

5. Introduction Cont.… Challenges : Faces challenges like handling sarcasm, irony, and ambiguous language. Context plays a significant role, making it difficult to accurately predict emotions without understanding the surrounding context. Future Trends : Advances in NLP and AI are improving text emotion analysis accuracy and reliability. Future trends include nuanced emotion understanding, cross-lingual analysis, and real-time emotion detection. 17-07-2024 Emotion Detection from Tweets Using Ensemble Models 5

6. Literature Survey 17-07-2024 Emotion Detection from Tweets Using Ensemble Models 6 SNo Author & Year Title Methodology Remarks 1 Poria et al., 2016 Aspect Extraction for Opinion Mining with a Deep Convolutional Neural Network Deep convolutional neural network (CNN) Effective for aspect-based sentiment analysis 2 Felbo et al., 2017 Using Millions of Emoji Occurrences to Learn Any-Domain Representations for Detecting Sentiment, Emotion, and Sarcasm Deep learning with emoji-based supervised learning Leveraged large-scale emoji data for emotion detection 3 Yadollahi et al., 2017 Current State of Text Sentiment Analysis from Opinion to Emotion Mining Survey of sentiment analysis techniques from opinion mining to emotion detection Explored the evolution from sentiment to emotion analysis 4 Zhang et al., 2018 Deep Learning for Sentiment Analysis: A Survey Review of deep learning methods, including CNNs and RNNs Comprehensive overview of deep learning approaches 5 Zhang et al., 2019 Sentiment Analysis: A Combined Approach Combined machine learning and lexicon-based methods Improved accuracy by integrating different techniques

7. Literature Survey Cont.… 17-07-2024 Emotion Detection from Tweets Using Ensemble Models 7 SNo Author & Year Title Methodology Remarks 6 Wang et al., 2020 HULK: An Energy-Efficient Heterogeneous Accelerator for Text Analysis via Convolutional Recurrent Networks Combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) Highlighted efficiency in emotion analysis computation 7 Xia et al., 2021 Sentiment and Emotion Classification with Multi-Task Learning Multi-task learning approach combining sentiment and emotion classification tasks Enhanced performance by leveraging shared representations 8 Liu et al., 2022 Transformer-based Model for Sentiment Analysis Transformer-based models (e.g., BERT, RoBERTa ) Achieved state-of-the-art results in various sentiment tasks 9 Chen et al., 2023 Emotion Detection in Text Using Graph Neural Networks Graph neural networks (GNNs) for capturing relationships between words Improved accuracy in detecting nuanced emotions 10 Patel & Kumar, 2024 Real-time Emotion Analysis in Social Media: Trends and Challenges Real-time processing and analysis of social media data using deep learning models Addressed challenges in scalability and real-time application

8. Methodology Data Processing: • Removing the URL and other characters: Eliminating URLs and non-alphanumeric characters ensures cleaner text data for subsequent analysis. • Remove Punctuations: Stripping away punctuation marks from the text helps in simplifying the data and removing noise. • Remove Stop Words: Removing common stop words such as "the," "and," "is" helps in focusing on meaningful content. • Normalization of the data: Normalizing the text data involves converting all words to lowercase. • Lemmatization: Lemmatization reduces words to their base or dictionary form. • Stemming: Stemming is the process of eliminating suffixes from words in order to get to their basic form. 17-07-2024 Emotion Detection from Tweets Using Ensemble Models 8

9. Methodology Cont.… Tokenization: Tf-idf (Term Frequency-Inverse Document Frequency): The Tf-idf value indicates how significant a term is in a given text in comparison to the entire corpus. It gives more weight to words that are common in a single document but rare in the corpus as a whole. Stylistic Feature (CV): Count Vectorization (CV), which stands for the frequency of each word in the text data, is used to extract stylistic elements. Sentiment Feature (Glove): In terms of sentiment characteristics, GloVe embeddings are utilized. These embeddings capture the semantic associations between words by analyzing their co-occurrence statistics. Tf-Idf + S + SE: This combination integrates Tf-idf, stylistic features, and sentiment features to leverage both term importance and semantic information in the text data. 17-07-2024 Emotion Detection from Tweets Using Ensemble Models 9

10. Methodology Cont.… Models: Random Forest: Ensemble learning method that constructs multiple decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees. MLP (Multilayer Perceptron): A type of artificial neural network composed of multiple layers of nodes, where each node is a neuron that uses non-linear activation functions. LightGBM : Gradient boosting framework that uses tree-based learning algorithms and is designed for efficiency, supporting large datasets and high-dimensional features. 17-07-2024 Emotion Detection from Tweets Using Ensemble Models 10

11. Methodology Cont.… The proposed approach employs ensemble learning techniques and a unique feature representation method to improve emotion recognition accuracy using user-generated Twitter data. To build input representations using stylistic, sentimental, and language elements retrieved from tweets, the system employs a Genetic Algorithm (GA). A weighted average soft-voting classifier that combines MLP, random forest, and LGBM classifiers is then used with the input representation. 17-07-2024 Emotion Detection from Tweets Using Ensemble Models 11

12. Results Analysis 17-07-2024 Emotion Detection from Tweets Using Ensemble Models 12 Classifier Precision Recall F1-Score Accuracy KNN 72 68 70 69 Decision Tree 75 70 72 71 Random Forest 82 78 80 79 XGBoost 85 81 83 82 SVM 80 77 78 78 RF + DT 83 79 81 80 XGBoost + DT 86 82 84 83 SVM + DT 81 78 79 78 RF + XGBoost 88 84 86 85 RF + SVM 83 79 81 80 XGBoost + SVM 87 83 85 84 RF + XGBoost + SVM 89 85 87 86 Voting Classifier (DT + AdaBoost) 84 80 82 81 Avg Soft-Voting Classifier (RF + MLP + LGBM) 95 95 96 98

13. Conclusion Our study introduces a novel emotion identification method using SSEL, integrating tweet stylistic, emotional, and language elements with a Genetic Algorithm for feature compression. We applied a weighted average Soft-voting classifier combining MLP, Random Forest, and LBGM to categorize tweets into six emotional categories, surpassing traditional classifiers and ensemble methods on a Twitter dataset. Our approach set a new benchmark in precision, recall, F1-score, and accuracy, with ensemble techniques enhancing performance by 98%. Future research will explore categorical and multi-emotion models, and validate our method across diverse user groups for real-world applicability. 17-07-2024 Emotion Detection from Tweets Using Ensemble Models 13