Analysis of Sentiment on Movie Reviews Using Word Embedding Self-Attentive LSTM

In the contemporary world, people share their thoughts rapidly in social media. Mining and extracting knowledge from this information for performing sentiment analysis is a complex task. Even though automated machine learning algorithms and techniques are available, and extraction of semantic and re...

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Veröffentlicht in:International journal of ambient computing and intelligence Jg. 12; H. 2; S. 33 - 52
Hauptverfasser: Sivakumar, Soubraylu, Rajalakshmi, Ratnavel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hershey IGI Global 01.04.2021
Schlagworte:
Algorithms
Analysis
Data mining
Embedding
Machine learning
Motion pictures
Movie reviews
Semantics
Sentiment analysis
Sparse matrices
Words (language)
India
ISSN:1941-6237, 1941-6245
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Zusammenfassung:In the contemporary world, people share their thoughts rapidly in social media. Mining and extracting knowledge from this information for performing sentiment analysis is a complex task. Even though automated machine learning algorithms and techniques are available, and extraction of semantic and relevant key terms from a sparse representation of the review is difficult. Word embedding improves the text classification by solving the problem of sparse matrix and semantics of the word. In this paper, a novel architecture is proposed by combining long short-term memory (LSTM) with word embedding to extract the semantic relationship between the neighboring words and also a weighted self-attention is applied to extract the key terms from the reviews. Based on the experimental analysis on the IMDB dataset, the authors have shown that the proposed architecture word-embedding self-attention LSTM architecture achieved an F1 score of 88.67%, while LSTM and word embedding LSTM-based models resulted in an F1 score of 84.42% and 85.69%, respectively.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1941-6237
1941-6245
DOI:10.4018/IJACI.2021040103