SEMANTIC SENTIMENT ANALYSIS BASED ON PROBABILISTIC GRAPHICAL MODELS AND RECURRENT NEURAL NETWORKS

SEMANTIC SENTIMENT ANALYSIS BASED ON PROBABILISTIC GRAPHICAL MODELS AND RECURRENT NEURAL NETWORKS

Abstract:
Semantic sentiment analysis is a vital task in natural language processing, aiming to extract and analyze the sentiment expressed in textual data. In recent years, the combination of probabilistic graphical models (PGMs) and recurrent neural networks (RNNs) has shown promising results in various NLP applications. This paper proposes a novel approach that integrates PGMs and RNNs for semantic sentiment analysis.

The proposed approach leverages the strengths of both PGMs and RNNs to capture the complex relationships between words in a sentence and model the sentiment expressed by those words. Initially, a PGM, such as a Markov Random Field (MRF) or a Conditional Random Field (CRF), is employed to represent the dependencies between neighboring words in the sentence. The PGM encodes contextual information and considers the global structure of the sentence, which helps in capturing long-range dependencies.

To further enhance the sentiment analysis performance, a recurrent neural network, such as a Long Short-Term Memory (LSTM) or a Gated Recurrent Unit (GRU), is incorporated into the framework. The RNN processes the sentence sequentially, considering the order of the words and capturing the temporal dependencies. It encodes the semantic information of each word and generates a sentiment representation.

The outputs of the PGM and RNN are then combined using a fusion mechanism to obtain a comprehensive sentiment prediction for the input sentence. This fusion can be achieved using techniques like weighted averaging, attention mechanisms, or multi-modal learning approaches.

Experiments on benchmark sentiment analysis datasets demonstrate the effectiveness of the proposed approach. The integration of PGMs and RNNs enables the model to capture both local and global dependencies, leading to improved sentiment classification accuracy. The approach also exhibits robustness in handling noisy and ambiguous sentiment expressions, making it suitable for real-world sentiment analysis tasks.

In conclusion, this paper presents a novel approach for semantic sentiment analysis that combines probabilistic graphical models and recurrent neural networks. The integration of these two techniques provides a powerful framework for capturing complex dependencies and modeling sentiment expressions in textual data. The proposed approach shows promising results and opens avenues for further research in sentiment analysis and other NLP tasks.

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