Innovative Hate Speech Detection in Code-Mixed Hindi-English Tweets through Deep Learning and Random Forest Algorithm

Problem Definition

.The problem of detecting hate speech in conversationally mixed Hindi and English tweets poses several key limitations and challenges. Identifying instances of hate speech in user-generated comments within these tweets is essential for creating a safer online environment. Additionally, the need for improved system accuracy in this process highlights the complexity of the task at hand. Existing techniques in data mining may not be sufficient to accurately detect hate speech in mixed-language tweets, further emphasizing the importance of developing effective and precise methods for this purpose.

The lack of comprehensive research in this domain poses a significant obstacle in achieving successful detection and classification of hate speech. Therefore, there is a critical need for innovative solutions that can navigate the nuances of multilingual conversations and accurately identify harmful content to address this pressing issue.

Objective

The objective is to develop innovative solutions to accurately detect hate speech in conversationally mixed Hindi and English tweets by utilizing a combination of data preprocessing, machine learning algorithms, and data visualization techniques. The proposed work aims to enhance hate speech detection accuracy and improve the overall efficiency of the system by leveraging the strengths of BERT, Deep Learning through LSTM model, and Random Forest Classifier algorithms in analyzing the complex language mix present in mixed-language tweets. By addressing the limitations and challenges posed by existing techniques, the objective is to create a safer online environment by accurately identifying harmful content in user-generated comments.

Proposed Work

The proposed work aims to address the challenge of hate speech detection within conversationally mixed Hindi and English tweets by utilizing a combination of data preprocessing, machine learning algorithms, and data visualization techniques. By uploading a data set of tweets onto Google Drive, preprocessing the content and applying labels, the system is able to analyze the language mix within the tweets. The use of the BERT machine learning algorithm allows for the calculation of various parameters to improve accuracy, precision, and overall performance of the hate speech detection system. By employing both Deep Learning through LSTM model and Random Forest Classifier algorithms, the system aims to refine the data analysis process and generate a more effective output. This comprehensive approach is intended to enhance the overall accuracy and efficiency of hate speech detection within mixed-language tweets.

The rationale behind the selection of specific techniques and algorithms lies in their proven effectiveness in handling natural language processing tasks and sentiment analysis, particularly in multilingual contexts. The use of BERT, known for its advanced natural language understanding capabilities, is well-suited for analyzing the complex language mix present in conversationally mixed Hindi and English tweets. Additionally, the incorporation of both Deep Learning and Random Forest Classifier algorithms allows for a more robust data analysis process, leveraging the strengths of each to improve hate speech detection accuracy. The visualization techniques, such as word cloud displays, further enhance the interpretability of the data and help in understanding the nature of the content being analyzed. By adopting this comprehensive approach, the proposed work aims to achieve the objectives of enhancing hate speech detection accuracy and improving the overall efficiency of the system.

Application Area for Industry

This project can be applied in various industrial sectors such as social media, online platforms, communication technology, and content moderation services. The proposed solutions can be particularly useful in addressing the challenges faced by these industries in identifying and combating hate speech within user-generated content. By applying advanced data mining techniques and machine learning algorithms like BERT, LSTM, and Random Forest Classifier, industries can improve the accuracy and efficiency of hate speech detection in mixed-language tweets. Implementing these solutions can result in more effective content moderation, increased user safety, and enhanced brand reputation for companies operating in these sectors. Additionally, the ability to accurately detect and classify hate speech can lead to better compliance with legal requirements and regulations related to online content moderation.

Application Area for Academics

The proposed project holds significant potential to enrich academic research, education, and training in various ways. Firstly, it addresses a pressing issue in the digital era – hate speech detection in mixed-language tweets, providing a real-world problem for researchers to tackle. The development and application of algorithms such as the LSTM model and Random Forest Classifier can offer valuable insights into the field of natural language processing and machine learning. In an educational setting, this project can serve as a hands-on learning experience for students in the fields of computer science, data science, and artificial intelligence. By working with real data and implementing cutting-edge algorithms, students can gain practical skills in data preprocessing, model training, and evaluation.

Moreover, the project can facilitate training in interdisciplinary research, as it involves both linguistic analysis and machine learning techniques. Researchers in the fields of sentiment analysis, social media mining, and hate speech detection can utilize the code and methodologies developed in this project for further studies and experiments. The dataset of mixed-language tweets and the trained models can serve as valuable resources for exploring innovative research methods and developing new approaches to tackle hate speech online. MTech students and PhD scholars can benefit from analyzing the project's literature and codebase to enhance their own research projects in related domains. Moving forward, the project's scope can be extended to incorporate more languages, develop advanced text classification techniques, and explore the impact of context on hate speech detection.

By continued research and collaboration in this area, the project can contribute to the advancement of technology-driven solutions for addressing online hate speech and promoting a safer digital environment.

Algorithms Used

The project utilizes the Deep Learning by LSTM Model algorithm for sequence prediction in tweets, capturing the conversational flow effectively. This algorithm helps in understanding the context and sentiment of the tweets, contributing to the accurate detection of hate speech. Additionally, the Random Forest Classifier algorithm is used to enhance the classification of hate speech by leveraging ensemble learning techniques. Through a combination of these algorithms, the project aims to achieve improved accuracy in detecting and categorizing hate speech in tweets, ultimately enhancing the efficiency of the overall process.

Keywords

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SEO Tags

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