Enhancing Twitter Sentiment Analysis using Hybrid Feature Selection and Advanced LSTM Model

Problem Definition

The existing sentiment analysis techniques for Twitter data have faced numerous limitations that have negatively impacted their accuracy and performance. One major drawback is the predominant use of machine learning classifiers, which may not be as effective as deep learning based classifiers in this context. Additionally, the techniques for feature selection have proven to be ineffective, leading to issues with dataset dimensionality. Furthermore, machine learning classifiers struggle to handle large Twitter datasets, often resulting in overfitting and reduced classification accuracy. Moreover, the imbalance in available datasets on the internet poses yet another challenge for accurate sentiment analysis.

In light of these limitations, it is evident that a novel sentiment analysis technique is necessary to address these issues and enhance the overall performance of sentiment analysis on Twitter data.

Objective

The objective of this project is to address the limitations of existing sentiment analysis techniques for Twitter data by proposing an improved model that utilizes deep learning algorithms. The aim is to enhance accuracy and performance by overcoming issues with machine learning classifiers, dataset imbalance, and dimensionality problems. The proposed work involves implementing a two-phase approach that includes enhancing data preprocessing techniques and utilizing a Long Short Term Memory (LSTM) model for sentiment classification. By incorporating a hybrid feature selection technique and advanced DL algorithms, the model seeks to improve overall system performance in sentiment analysis on Twitter data.

Proposed Work

In this project, the main aim is to address the limitations of existing sentiment analysis (SA) techniques by proposing an improved SA model that utilizes deep learning (DL) algorithms for more efficient results. The problem definition outlined the gaps in current SA methods, highlighting the issues with ML classifiers, dataset imbalance, and dimensionality problems. The proposed work involves implementing a two-phase approach where data preprocessing techniques are enhanced, and a DL-based Long Short Term Memory (LSTM) model is used for sentiment classification. By integrating a hybrid feature selection technique combining chi-square and extra tree models, the proposed model aims to reduce dataset dimensionality while retaining critical information, ultimately improving accuracy and lowering processing time. Through the use of LSTM, the model can effectively classify opinions in tweets into positive, negative, and neutral categories with high accuracy, thus addressing the limitations identified in existing SA methods.

By incorporating advanced DL algorithms such as LSTM, the proposed model aims to enhance sentiment analysis by focusing on crucial characteristics for pattern recognition, which in turn will improve overall system performance. Additionally, the project utilizes a Twitter dataset accessed from Kaggle.com for testing and validation purposes, but the dataset undergoes preprocessing techniques such as tokenization and stemming to address imbalance issues. The rationale behind choosing specific techniques such as the hybrid feature selection method and LSTM is to overcome the limitations of existing SA models, offering a more accurate and efficient approach to sentiment analysis. The project's approach involves a systematic process of data preparation, feature selection, and DL-based classification to achieve the objectives of increasing accuracy, reducing complexity, and enhancing system performance in sentiment analysis.

Application Area for Industry

This project can be applied across a wide range of industrial sectors including social media marketing, customer service, market research, and reputation management. By implementing the proposed solutions such as the use of DL based LSTM classifiers, hybrid feature selection techniques, and efficient data pre-processing methods, industries can overcome the limitations faced by existing sentiment analysis systems. Specifically, industries can benefit from higher accuracy rates in sentiment detection, reduced dataset dimensionality, improved handling of large datasets, and enhanced classification of tweets into positive, negative, and neutral categories. Overall, the integration of these advanced techniques can lead to better decision-making, improved customer satisfaction, and more effective communication strategies within various industrial domains.

Application Area for Academics

The proposed project on sentiment analysis of Tweets using a hybrid feature selection technique and LSTM-based deep learning model has the potential to enrich academic research, education, and training in various ways. In terms of academic research, this project can contribute to the development of innovative research methods in the field of sentiment analysis and natural language processing. By addressing the limitations of existing sentiment analysis techniques, researchers can explore new avenues for improving accuracy and efficiency in sentiment detection from social media data. For education and training, this project can serve as a valuable tool for teaching students about advanced techniques in data analysis, machine learning, and deep learning. By providing code implementations and literature on the proposed methodology, educators can facilitate hands-on learning experiences for students interested in sentiment analysis and related research areas.

The relevance and potential applications of this project in educational settings lie in its ability to demonstrate the importance of feature selection techniques, deep learning models, and data preprocessing methods in enhancing the accuracy of sentiment analysis systems. By showcasing the impact of these techniques on real-world Twitter data, educators can inspire students to explore similar approaches in their own research projects. This project can be particularly beneficial for researchers, MTech students, and PhD scholars in the field of artificial intelligence, machine learning, and computational linguistics. They can utilize the code and literature provided in this project to implement similar methodologies in their own research work, thereby advancing the state-of-the-art in sentiment analysis and social media analytics. In terms of future scope, researchers can further extend this project by exploring different feature selection techniques, experimenting with other deep learning models, and analyzing the impact of sentiment analysis on diverse social media platforms.

By continuously refining and expanding upon the proposed methodology, this project can pave the way for new research directions and applications in sentiment analysis research.

Algorithms Used

SelectKBest feature selection algorithm is used in the project to select the most important features from the dataset. This algorithm helps in reducing the dimensionality of the dataset and improving the accuracy of the sentiment analysis model by retaining only the critical information. Extra Trees Classifier algorithm is utilized to further enhance the feature selection process in the project. By integrating this algorithm with the SelectKBest feature selection, the dataset is optimized to contain only essential data for sentiment analysis, improving efficiency and accuracy of the model. Deep learning technique, specifically Long Term Short Memory (LSTM), is incorporated in the project for identifying and categorizing sentiments from Tweets into positive, negative, and neutral.

LSTM helps in retaining and memorizing crucial characteristics for pattern recognition, thereby increasing the accuracy of the sentiment analysis model. This advanced version of RNNs improves the efficiency and effectiveness of sentiment analysis by recognizing opinions with high accuracy.

Keywords

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SEO Tags

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