Movie Review Based Sentiment Analysis


Authors : A. Ajith Kumar; P. Madhu Charan Reddy; Nikhil Gunnam

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/3eed3x68

Scribd : https://tinyurl.com/uc7nnsk9

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG345

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Abstract : Sentiment analysis, an automated method for detecting emotions and opinions in text, has become a versatile tool applied across various domains, from assessing online customer feedback to monitoring sentiment trends on social media. While Bag of Words (BoW) has been the traditional method, more advanced techniques like Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) have revolutionized sentiment analysis by elevating accuracy and context comprehension. This study conducts an extensive comparison between BERT, a hybrid model that combines CNN and LSTM in the realm of sentiment analysis within movie reviews. Through meticulous experimentation, the study strongly proves that both the hybrid model of CNN and LSTM models significantly outshine the techniques like BERT and BoW with noticeably higher accuracy in precisely categorizing sentiments expressed in movie reviews. These results highlight how deep learning methodologies remarkably refine the precision and effectiveness of sentiment analysis, allowing for more nuanced and context-aware sentiment classification. The study's significance goes beyond performance contrasts; it demonstrates the exceptional capacity of BERT along with the hybrid model of CNN and LSTM to comprehend the complexities of language and contextual nuances within movie reviews. This heightened contextual understanding is particularly crucial in sentiment analysis, enabling the models to discern subtle shifts in sentiment that simpler methods like BoW might overlook. By showcasing the supremacy of hybrid model that combines CNN and LSTM in movie review sentiment analysis, this research opens new avenues for the field's advancement. It emphasizes the potential of deep learning techniques in revolutionizing sentiment analysis across diverse domains, offering more accurate and contextually attuned sentiment classification for deeper insights and well-informed decision-making. This study stands as a valuable asset for researchers and practitioners aiming to leverage deep learning for sentiment analysis applications

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Sentiment analysis, an automated method for detecting emotions and opinions in text, has become a versatile tool applied across various domains, from assessing online customer feedback to monitoring sentiment trends on social media. While Bag of Words (BoW) has been the traditional method, more advanced techniques like Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) have revolutionized sentiment analysis by elevating accuracy and context comprehension. This study conducts an extensive comparison between BERT, a hybrid model that combines CNN and LSTM in the realm of sentiment analysis within movie reviews. Through meticulous experimentation, the study strongly proves that both the hybrid model of CNN and LSTM models significantly outshine the techniques like BERT and BoW with noticeably higher accuracy in precisely categorizing sentiments expressed in movie reviews. These results highlight how deep learning methodologies remarkably refine the precision and effectiveness of sentiment analysis, allowing for more nuanced and context-aware sentiment classification. The study's significance goes beyond performance contrasts; it demonstrates the exceptional capacity of BERT along with the hybrid model of CNN and LSTM to comprehend the complexities of language and contextual nuances within movie reviews. This heightened contextual understanding is particularly crucial in sentiment analysis, enabling the models to discern subtle shifts in sentiment that simpler methods like BoW might overlook. By showcasing the supremacy of hybrid model that combines CNN and LSTM in movie review sentiment analysis, this research opens new avenues for the field's advancement. It emphasizes the potential of deep learning techniques in revolutionizing sentiment analysis across diverse domains, offering more accurate and contextually attuned sentiment classification for deeper insights and well-informed decision-making. This study stands as a valuable asset for researchers and practitioners aiming to leverage deep learning for sentiment analysis applications

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